Publications | Xuhang Chen

Conference Articles

2025

Multi-modal Mood Reader: Pre-trained Model Empowers Cross-Subject Emotion Recognition

Yihang Dong , Xuhang Chen, Yanyan Shen, Michael Kwok-po Ng, Tao Qian, and Shuqiang Wang^†

In Neural Computing for Advanced Applications, 2025

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Emotion recognition based on Electroencephalography (EEG) has gained significant attention and diversified development in fields such as neural signal processing and affective computing. However, the unique brain anatomy of individuals leads to non-negligible natural differences in EEG signals across subjects, posing challenges for cross-subject emotion recognition. While recent studies have attempted to address these issues, they still face limitations in practical effectiveness and model framework unity. Current methods often struggle to capture the complex spatial-temporal dynamics of EEG signals and fail to effectively integrate multimodal information, resulting in suboptimal performance and limited generalizability across subjects. To overcome these limitations, we develop a Pre-trained model based Multimodal Mood Reader for cross-subject emotion recognition that utilizes masked brain signal modeling and interlinked spatial-temporal attention mechanism. The model learns universal latent representations of EEG signals through pre-training on large scale dataset, and employs Interlinked spatial-temporal attention mechanism to process Differential Entropy (DE) features extracted from EEG data. Subsequently, a multi-level fusion layer is proposed to integrate the discriminative features, maximizing the advantages of features across different dimensions and modalities. Extensive experiments on public datasets demonstrate Mood Reader’s superior performance in cross-subject emotion recognition tasks, outperforming state-of-the-art methods. Additionally, the model is dissected from attention perspective, providing qualitative analysis of emotion-related brain areas, offering valuable insights for affective research in neural signal processing.
@inproceedings{Dong:2025, title = {Multi-modal Mood Reader: Pre-trained Model Empowers Cross-Subject Emotion Recognition}, author = {Dong, Yihang and Chen, Xuhang and Shen, Yanyan and Ng, Michael Kwok-po and Qian, Tao and Wang, Shuqiang}, year = {2025}, booktitle = {Neural Computing for Advanced Applications}, publisher = {Springer}, address = {Guilin, China}, pages = {178-192}, doi = {10.1007/978-981-97-7007-6_13}, }
WACV
High-Fidelity Document Stain Removal via A Large-Scale Real-World Dataset and A Memory-Augmented Transformer

Mingxian Li^*, Hao Sun^*, Yingtie Lei^*, Xiaofeng Zhang, Yihang Dong, Yilin Zhou , Zimeng Li , and Xuhang Chen^†

In Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision (WACV), 2025

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Document images are often degraded by various stains, significantly impacting their readability and hindering downstream applications such as document digitization and analysis. The absence of a comprehensive stained document dataset has limited the effectiveness of existing document enhancement methods in removing stains while preserving fine-grained details. To address this challenge, we construct StainDoc, the first large-scale, high-resolution (2145\times2245) dataset specifically designed for document stain removal. StainDoc comprises over 5,000 pairs of stained and clean document images across multiple scenes. This dataset encompasses a diverse range of stain types, severities, and document backgrounds, facilitating robust training and evaluation of document stain removal algorithms. Furthermore, we propose StainRestorer, a Transformer-based document stain removal approach. StainRestorer employs a memory-augmented Transformer architecture that captures hierarchical stain representations at part, instance, and semantic levels via the DocMemory module. The Stain Removal Transformer (SRTransformer) leverages these feature representations through a dual attention mechanism: an enhanced spatial attention with an expanded receptive field, and a channel attention captures channel-wise feature importance. This combination enables precise stain removal while preserving document content integrity. Extensive experiments demonstrate StainRestorer’s superior performance over state-of-the-art methods on the StainDoc dataset and its variants StainDoc_Mark and StainDoc_Seal, establishing a new benchmark for document stain removal. Our work highlights the potential of memory-augmented Transformers for this task and contributes a valuable dataset to advance future research.
@inproceedings{Li:2025, title = {High-Fidelity Document Stain Removal via A Large-Scale Real-World Dataset and A Memory-Augmented Transformer}, author = {Li, Mingxian and Sun, Hao and Lei, Yingtie and Zhang, Xiaofeng and Dong, Yihang and Zhou, Yilin and Li, Zimeng and Chen, Xuhang}, booktitle = {Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision (WACV)}, year = {2025}, pages = {7614-7624}, publisher = {IEEE}, address = {Tucson, AZ, USA}, doi = {10.1109/WACV61041.2025.00740}, }
Cross-View Geo-Localization via Learning Correspondence Semantic Similarity Knowledge

Guanli Chen , Guoheng Huang^†, Xiaochen Yuan , Xuhang Chen, Guo Zhong, and Chi-Man Pun

In International Conference on Multimedia Modeling (MMM), 2025

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Cross-view geo-localization aims at retrieving and estimating accurate geographic locations from ground images in a geo-tagged aerial image database. Existing approaches focus on two independent two-branch models to learn fine-grained representations of perspectives, neglecting to learn more discriminative representations through interactions. In this paper, we propose the GeoSSK method, which adapts the learning process of the model by learning local semantic similarity information between aerial and ground pairs via a new interaction module. We then transfer the semantic similarity knowledge learned during the interaction process to the student model through knowledge distillation. Specifically, we design a Cross-fusion Interaction Module (CIM) based on cross-attention, which learns local semantic similarity information between perspectives to adjust the learning of the model. Meanwhile, considering the presence of visual distractions in complex environments, we adjust the degree of interaction between perspectives by the Contribution Factor (CF) of the local representation to the global representation. In addition, we introduce Semantic Similarity Knowledge Distillation (SSKD) between teachers and students for cross-view geo-localization. The interaction learning model serves as the teacher, transferring its semantic similarity knowledge to the student. At the same time, we designed an Incorrect Knowledge Filter (IKF) to filter incorrect knowledge of teachers. Experimental results demonstrate the effectiveness and competitive performance of GeoSSK.
@inproceedings{Chen:2026, author = {Chen, Guanli and Huang, Guoheng and Yuan, Xiaochen and Chen, Xuhang and Zhong, Guo and Pun, Chi-Man}, title = {Cross-View Geo-Localization via Learning Correspondence Semantic Similarity Knowledge}, booktitle = {International Conference on Multimedia Modeling (MMM)}, year = {2025}, publisher = {Springer}, address = {Nara, Japan}, pages = {220--233}, doi = {10.1007/978-981-96-2054-8_17}, }
ICASSP
Underwater Image Restoration via Polymorphic Large Kernel CNNs

Xiaojiao Guo^*, Yihang Dong^* , Xuhang Chen^*†, Weiwen Chen , Zimeng Li^†, Fuchen Zheng, and Chi-Man Pun^†

In Proceedings of the IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP), 2025

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Underwater Image Restoration (UIR) remains a challenging task in computer vision due to the complex degradation of images in underwater environments. While recent approaches have leveraged various deep learning techniques, including Transformers and complex, parameter-heavy models to achieve significant improvements in restoration effects, we demonstrate that pure CNN architectures with lightweight parameters can achieve comparable results. In this paper, we introduce UIR-PolyKernel, a novel method for underwater image restoration that leverages Polymorphic Large Kernel CNNs. Our approach uniquely combines large kernel convolutions of diverse sizes and shapes to effectively capture long-range dependencies within underwater imagery. Additionally, we introduce a Hybrid Domain Attention module that integrates frequency and spatial domain attention mechanisms to enhance feature importance. By leveraging the frequency domain, we can capture hidden features that may not be perceptible to humans but are crucial for identifying patterns in both underwater and on-air images. This approach enhances the generalization and robustness of our UIR model. Extensive experiments on benchmark datasets demonstrate that UIR-PolyKernel achieves state-of-the-art performance in underwater image restoration tasks, both quantitatively and qualitatively. Our results show that well-designed pure CNN architectures can effectively compete with more complex models, offering a balance between performance and computational efficiency. This work provides new insights into the potential of CNN-based approaches for challenging image restoration tasks in underwater environments. The code is available at https://github.com/CXH-Research/UIR-PolyKernel.
@inproceedings{Guo:2025, author = {Guo, Xiaojiao and Dong, Yihang and Chen, Xuhang and Chen, Weiwen and Li, Zimeng and Zheng, Fuchen and Pun, Chi-Man}, title = {Underwater Image Restoration via Polymorphic Large Kernel CNNs}, booktitle = {Proceedings of the IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP)}, pages = {1--5}, year = {2025}, publisher = {IEEE}, address = {Hyderabad, India}, doi = {10.1109/ICASSP49660.2025.10890803}, }
FOPS-V: Feature-Aware Optimization and Parallel Scale Fusion for 3D Human Reconstruction in Video

Yang Huang , Guoheng Huang^†, Lianglun Cheng, Yejing Huo , Xuhang Chen, Xiaochen Yuan, Guo Zhong, and Chi-Man Pun

In International Conference on Neural Information Processing (ICONIP), 2025

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Video-based 3D human reconstruction, a fundamental task in computer vision, aims to accurately estimate the 3D pose and shape of the human body from video sequences. While recent methods leverage spatial and temporal feature extraction techniques, many remain limited by single-scale processing, hindering their performance in complex scenes. Additionally, challenges such as occlusion and complex poses often lead to inaccurate reconstructions. To address these limitations, we propose FOPS-V: Feature-aware Optimization and Parallel Scale Fusion for 3D Human Reconstruction in Video. Our approach comprises three key components: a Feature-Aware Optimization (FAO) block, a Parallel Scale-Aware Attention (PSAA) block, and a Normalized Feature-Aware Representation (NFAR) guided by Feature-Response Layer Normalization (FRLN). The FAO block enhances feature extraction by optimizing joint and mesh vertex representations through the fusion of image features and learned query vectors. The PSAA block performs subscale feature extraction for joint and mesh vertices and fuses multiscale feature information to improve pose and shape representations. Guided by FRLN, the NFAR addresses instability caused by variations in feature statistics within the FAO and PSAA blocks. This normalization, with an adaptable threshold, enhances robustness to noisy or outlier data, preventing performance degradation. Extensive evaluations on the 3DPW, MPI-INF-3DHP, and Human3.6M datasets demonstrate that FOPS-V outperforms state-of-the-art methods, highlighting its effectiveness for 3D human reconstruction in video.
@inproceedings{Huang:2025, title = {FOPS-V: Feature-Aware Optimization and Parallel Scale Fusion for 3D Human Reconstruction in Video}, author = {Huang, Yang and Huang, Guoheng and Cheng, Lianglun and Huo, Yejing and Chen, Xuhang and Yuan, Xiaochen and Zhong, Guo and Pun, Chi-Man}, year = {2025}, booktitle = {International Conference on Neural Information Processing (ICONIP)}, publisher = {Springer}, address = {Auckland, New Zealand}, pages = {180--194}, doi = {10.1007/978-981-96-6596-9_13}, }
Advancing Comic Image Inpainting: A Novel Dual-Stream Fusion Approach with Texture Enhancements

Zilan Hong, Lianglun Cheng , Guoheng Huang^† , Xuhang Chen, Chi-Man Pun, Xiaochen Yuan, and Guo Zhong

In International Conference on Neural Information Processing (ICONIP), 2025

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In the process of comic localization, a crucial step is to fill in the pixels obscured due to the removal of dialogue boxes or sound effect text. Comic inpainting is more challenging than natural images. On one hand, its structure and texture are highly abstract, which confuses semantic interpretation and content synthesis. On the other hand, high-frequency information specific to comic images (such as lines and dots) is crucial for visual representation. This paper proposes the Texture-Structure Fusion Network (TSF-Net) with dual-stream encoder, introducing the Dual-stream Space-Gated Fusion (DSSGF) module for effective feature interaction. Additionally, a Multi-scale Histogram Texture Enhancement (MHTE) module is designed to enhance texture information aggregation dynamically. Visual comparisons and quantitative experiments demonstrate the effectiveness of the method, proving its superiority over existing techniques in comic inpainting. The implementation methods and dataset can be obtained from https://github.com/arashi-knight/comic-inpaint-pre.
@inproceedings{Hong:2025, title = {Advancing Comic Image Inpainting: A Novel Dual-Stream Fusion Approach with Texture Enhancements}, author = {Hong, Zilan and Cheng, Lianglun and Huang, Guoheng and Chen, Xuhang and Pun, Chi-Man and Yuan, Xiaochen and Zhong, Guo}, year = {2025}, booktitle = {International Conference on Neural Information Processing (ICONIP)}, publisher = {Springer}, address = {Auckland, New Zealand}, pages = {181--195}, doi = {10.1007/978-981-96-6969-1_13}, }
RST-UNet: Medical Image Segmentation Transformer Effectively Combining Superpixel

Songhang Ye, Zhoule Feng , Guoheng Huang, Jinghong Ke , Xuhang Chen, Chi-Man Pun, Guo Zhong, and Xiaochen Yuan

In International Conference on Neural Information Processing (ICONIP), 2025

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Medical image segmentation has advanced with models like UCTransNet, TransUNet, and TransClaw U-Net, which integrate Convolutional Neural Networks (CNNs) and Vision Transformers (ViTs). However, these models face limitations due to the locality of convolutions and the computational demands of Transformers. To overcome these challenges, we introduce RST-UNet, an innovative encoder-decoder network that balances effectiveness with computational efficiency. RST-UNet features two groundbreaking innovations: the Compact Representation Block (CRB) and the Compact Dependency Modeling Block (CDMB). The CRB utilizes superpixel pooling to capture long-range dependencies while minimizing parameters and computation time. The CDMB integrates superpixel unpooling with attention mechanisms and Rotary Position Embedding (RoPE) to enhance long-range dependency modeling. This approach emphasizes critical regions and leverages RoPE to capture extensive image dependencies effectively. Our experimental results on publicly available synapse datasets highlight RST-UNet’s exceptional performance, particularly in segmenting small organs such as the gallbladder, right kidney, and pancreas. Remarkably, RST-UNet achieves superior results without pre-training, showcasing its high adaptability for diverse medical image segmentation tasks. This work represents a significant advancement in developing efficient and effective algorithms for medical image analysis.
@inproceedings{Ye:2025, title = {RST-UNet: Medical Image Segmentation Transformer Effectively Combining Superpixel}, author = {Ye, Songhang and Feng, Zhoule and Huang, Guoheng and Ke, Jinghong and Chen, Xuhang and Pun, Chi-Man and Zhong, Guo and Yuan, Xiaochen}, year = {2025}, booktitle = {International Conference on Neural Information Processing (ICONIP)}, publisher = {Springer}, address = {Auckland, New Zealand}, pages = {336--350}, doi = {10.1007/978-981-96-6969-1_23}, }
ROSAL: Semi-supervised Active Learning with Representation Aggregation and Outlier for Endoscopy Image Classification

Xiaocong Huang , Guoheng Huang^†, Guo Zhong, Xiaochen Yuan , Xuhang Chen, Chi-Man Pun , and Jianwu Chen

In International Conference on Neural Information Processing (ICONIP), 2025

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The classification of endoscopy images is vital for earlydetection and prevention of Colorectal Cancer (CRC). However, manual annotation of these images is expensive. Semi-supervised ActiveLearning (SAL) can help reduce costs, but issues with the accuracyof pseudo-labels and the tendency to over-select outliers remain. Toaddress these, we introduce ROSAL, a new SAL framework featuringRepresentational Correlation-based Pseudo-label Training (RCPT) andOutlier-based Hybrid Querying (OHQ). RCPT employs a pseudo-labelcontrastive loss to enhance agreement among unlabeled data representations and reduce discord. The pseudo-label generator in RCPT leveragesthis correlation for more precise labeling. OHQ introduces a distancefactor to minimize outlier selection through a hybrid querying strategy. Experimental results demonstrate that ROSAL outperforms otheractive learning methods, achieving 71.46% and 90.79% accuracy on apublicly available endoscopic dataset and a publicly available naturalimage dataset, respectively, using only 40% and 20% of the labeled data.
@inproceedings{Huang:2026, title = {ROSAL: Semi-supervised Active Learning with Representation Aggregation and Outlier for Endoscopy Image Classification}, author = {Huang, Xiaocong and Huang, Guoheng and Zhong, Guo and Yuan, Xiaochen and Chen, Xuhang and Pun, Chi-Man and Chen, Jianwu}, year = {2025}, booktitle = {International Conference on Neural Information Processing (ICONIP)}, publisher = {Springer}, address = {Auckland, New Zealand}, pages = {350--364}, doi = {10.1007/978-981-96-6606-5_24}, }
MMCIE: Multi-order Multivariate Coalition Internal-External Interaction for Image Explanation

Mingfeng Huang , Guoheng Huang^†, Xiaomin Zhou, Chi-Man Pun , Xuhang Chen, Xiaochen Yuan, and Guo Zhong

In International Conference on Neural Information Processing (ICONIP), 2025

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Shapley values are extensively utilized in Explainable Artificial Intelligence to interpret predictions made by complex machine learning models. However, much of the research has focused on addressing the costly computational aspects of Shapley values, neglecting the rich interactive knowledge representation inherent in the data within the model. Therefore, MMCIE is proposed in this article, focusing on exploring the conceptual modeling of data by the model. Based on Shapley values, it defines the interaction between inner and outer coalitions to accurately quantify the concept of model modeling. Specifically, the Superpixel Coalitions Selecting Module (SCSM) is proposed, which defines coalitions as entities and paves the way for calculating multivariable interaction values and internal interaction values. Additionally, the Internal and External Interaction Module (IEIM) acts as a bridge, computing and connecting the internal and external interaction values of coalitions, thereby constructing the feature prototype of the model. Moreover, the Multi-order Equidistance Coalitions Modeling Module (MECMM) is introduced as an effective approach to reduce computational complexity and explore the storage methods of the model’s conceptual modeling. Experiments on two datasets show the advantage of MMCIE over existing methods, revealing the model’s concept modeling process through multi-order interactions and offering a fresh view for model interpretation.
@inproceedings{Huang:2027, title = {MMCIE: Multi-order Multivariate Coalition Internal-External Interaction for Image Explanation}, author = {Huang, Mingfeng and Huang, Guoheng and Zhou, Xiaomin and Pun, Chi-Man and Chen, Xuhang and Yuan, Xiaochen and Zhong, Guo}, year = {2025}, booktitle = {International Conference on Neural Information Processing (ICONIP)}, publisher = {Springer}, address = {Auckland, New Zealand}, pages = {1--15}, editor = {Mahmud, Mufti and Doborjeh, Maryam and Wong, Kevin and Leung, Andrew Chi Sing and Doborjeh, Zohreh and Tanveer, M.}, doi = {10.1007/978-981-96-6948-6_1}, }

2024

AAAI
Devignet: High-Resolution Vignetting Removal via a Dual Aggregated Fusion Transformer with Adaptive Channel Expansion

Shenghong Luo^* , Xuhang Chen^*, Weiwen Chen, Zinuo Li, Shuqiang Wang^†, and Chi-Man Pun^†

In Proceedings of the AAAI Conference on Artificial Intelligence (AAAI), 2024

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Vignetting commonly occurs as a degradation in images resulting from factors such as lens design, improper lens hood usage, and limitations in camera sensors. This degradation affects image details, color accuracy, and presents challenges in computational photography. Existing vignetting removal algorithms predominantly rely on ideal physics assumptions and hand-crafted parameters, resulting in the ineffective removal of irregular vignetting and suboptimal results. Moreover, the substantial lack of real-world vignetting datasets hinders the objective and comprehensive evaluation of vignetting removal. To address these challenges, we present VigSet, a pioneering dataset for vignetting removal. VigSet includes 983 pairs of both vignetting and vignetting-free high-resolution (over 4k) real-world images under various conditions. In addition, We introduce DeVigNet, a novel frequency-aware Transformer architecture designed for vignetting removal. Through the Laplacian Pyramid decomposition, we propose the Dual Aggregated Fusion Transformer to handle global features and remove vignetting in the low-frequency domain. Additionally, we propose the Adaptive Channel Expansion Module to enhance details in the high-frequency domain. The experiments demonstrate that the proposed model outperforms existing state-of-the-art methods. The code, models, and dataset are available at https://github.com/CXH-Research/DeVigNet.
@inproceedings{Luo:2024, title = {Devignet: High-Resolution Vignetting Removal via a Dual Aggregated Fusion Transformer with Adaptive Channel Expansion}, booktitle = {Proceedings of the AAAI Conference on Artificial Intelligence (AAAI)}, author = {Luo, Shenghong and Chen, Xuhang and Chen, Weiwen and Li, Zinuo and Wang, Shuqiang and Pun, Chi-Man}, year = {2024}, publisher = {Association for the Advancement of Artificial Intelligence}, address = {Vancouver, Canada}, pages = {4000-4008}, doi = {10.1609/AAAI.V38I5.28193}, }
PRCV
Medprompt: Cross-modal prompting for multi-task medical image translation

Xuhang Chen , Shenghong Luo, Chi-Man Pun^†, and Shuqiang Wang^†

In Proceedings of the Chinese Conference on Pattern Recognition and Computer Vision (PRCV), 2024

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The ability to translate medical images across different modalities is crucial for synthesizing missing data and aiding in clinical diagnosis. However, existing learning-based techniques have limitations when it comes to capturing cross-modal and global features. These techniques are often tailored to specific pairs of modalities, limiting their practical utility, especially considering the variability of missing modalities in different cases. In this study, we introduce MedPrompt, a multi-task framework designed to efficiently translate diverse modalities. Our framework incorporates the Self-adaptive Prompt Block, which dynamically guides the translation network to handle different modalities effectively. To encode the cross-modal prompt efficiently, we introduce the Prompt Extraction Block and the Prompt Fusion Block. Additionally, we leverage the Transformer model to enhance the extraction of global features across various modalities. Through extensive experimentation involving five datasets and four pairs of modalities, we demonstrate that our proposed model achieves state-of-the-art visual quality and exhibits excellent generalization capability. The results highlight the effectiveness and versatility of MedPrompt in addressing the challenges associated with cross-modal medical image translation.
@inproceedings{Chen:2025, title = {Medprompt: Cross-modal prompting for multi-task medical image translation}, author = {Chen, Xuhang and Luo, Shenghong and Pun, Chi-Man and Wang, Shuqiang}, year = {2024}, booktitle = {Proceedings of the Chinese Conference on Pattern Recognition and Computer Vision (PRCV)}, pages = {61--75}, doi = {10.1007/978-981-97-8496-7_5}, publisher = {Springer}, address = {Urumqi, China}, }
Structural Brain Network Generation via Brain Denoising Diffusion Probabilistic Model

Hongjie Jiang , Xuhang Chen, Changhong Jing, and Shuqiang Wang^†

In International Conference on AI in Healthcare (AIiH), 2024

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Alzheimer’s disease (AD) significantly impairs the quality of life for a vast patient population and poses treatment challenges, partly due to its elusive pathophysiological mechanisms. The analysis of structural brain networks is fundamental to elucidating these mechanisms. However, acquiring data on such networks is non-trivial, hindered by the slow generation of structural brain network data and the complexities involved in obtaining DTI, a key requisite for their construction. In this study, we introduce a brain denoising diffusion probabilistic model designed to synthesize structural brain networks at various stages of AD, thereby mitigating the difficulties inherent in data acquisition. We trained this model on the ADNI dataset, utilizing two sets of data: one comprising structural brain networks produced by PANDA, and the other amalgamating these PANDA-generated networks with those synthesized by our model. Both datasets were employed to train a DiffPool for subsequent diagnostic tasks. It can be seen that the brain networks generated by the brain denoising diffusion probabilistic model are beneficial for structural brain networks in downstream diagnostic tasks.
@inproceedings{Jiang:2024, title = {Structural Brain Network Generation via Brain Denoising Diffusion Probabilistic Model}, author = {Jiang, Hongjie and Chen, Xuhang and Jing, Changhong and Wang, Shuqiang}, booktitle = {International Conference on AI in Healthcare (AIiH)}, pages = {264--277}, year = {2024}, publisher = {Springer}, address = {Swansea, United Kingdom}, doi = {10.1007/978-3-031-67278-1_21}, }
MM
Dual-Hybrid Attention Network for Specular Highlight Removal

Xiaojiao Guo^* , Xuhang Chen^* , Shenghong Luo, Shuqiang Wang^†, and Chi-Man Pun^†

In Proceedings of the ACM International Conference on Multimedia (MM), 2024

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Specular highlights are a common issue in images captured under direct light sources. They are caused by the reflection of light sources on the surface of objects, which can lead to overexposure and loss of detail. Existing methods for specular highlight removal often rely on hand-crafted features and heuristics, which limits their effectiveness. In this paper, we propose a dual-hybrid attention network for specular highlight removal. The network consists of two branches: a spatial attention branch and a channel attention branch. The spatial attention branch focuses on the spatial distribution of specular highlights, while the channel attention branch emphasizes the importance of different channels. The two branches are combined to form a dual-hybrid attention network, which effectively removes specular highlights while preserving image details. Experimental results show that the proposed network outperforms state-of-the-art methods in terms of both visual quality and quantitative metrics.
@inproceedings{Guo:2024, title = {Dual-Hybrid Attention Network for Specular Highlight Removal}, author = {Guo, Xiaojiao and Chen, Xuhang and Luo, Shenghong and Wang, Shuqiang and Pun, Chi-Man}, booktitle = {Proceedings of the ACM International Conference on Multimedia (MM)}, year = {2024}, address = {Melbourne, VIC, Australia}, publisher = {ACM}, pages = {10173--10181}, doi = {10.1145/3664647.3680745}, }
BIBM
SMAFormer: Synergistic Multi-Attention Transformer for Medical Image Segmentation

Fuchen Zheng , Xuhang Chen, Weihuang Liu , Haolun Li, Yingtie Lei, Jiahui He, Chi-Man Pun^†, and Shounjun Zhou^†

In Proceedings of the IEEE International Conference on Bioinformatics and Biomedicine (BIBM), 2024

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In medical image segmentation, specialized computer vision techniques, notably transformers grounded in attention mechanisms and residual networks employing skip connections, have been instrumental in advancing performance. Nonetheless, previous models often falter when segmenting small, irregularly shaped tumors. To this end, we introduce SMAFormer, an efficient, Transformer-based architecture that fuses multiple attention mechanisms for enhanced segmentation of small tumors and organs. SMAFormer can capture both local and global features for medical image segmentation. The architecture comprises two pivotal components. First, a Synergistic Multi-Attention (SMA) Transformer block is proposed, which has the benefits of Pixel Attention, Channel Attention, and Spatial Attention for feature enrichment. Second, addressing the challenge of information loss incurred during attention mechanism transitions and feature fusion, we design a Feature Fusion Modulator. This module bolsters the integration between the channel and spatial attention by mitigating reshaping-induced information attrition. To evaluate our method, we conduct extensive experiments on various medical image segmentation tasks, including liver with tumor and bladder with tumor segmentation, achieving state-of-the-art results. Code and models are available at: \urlhttps://github.com/lzeeorno/SMAFormer.
@inproceedings{Zheng:2024, title = {SMAFormer: Synergistic Multi-Attention Transformer for Medical Image Segmentation}, author = {Zheng, Fuchen and Chen, Xuhang and Liu, Weihuang and Li, Haolun and Lei, Yingtie and He, Jiahui and Pun, Chi-Man and Zhou, Shounjun}, booktitle = {Proceedings of the IEEE International Conference on Bioinformatics and Biomedicine (BIBM)}, year = {2024}, pages = {4048-4053}, publisher = {IEEE}, address = {Lisbon, Portugal}, doi = {10.1109/BIBM62325.2024.10822736}, }
BIBM
IMAN: An Adaptive Network for Robust NPC Mortality Prediction with Missing Modalities

Yejing Huo , Guoheng Huang^†, Lianglun Cheng, Jianbin He , Xuhang Chen, Xiaochen Yuan, Guo Zhong, and Chi-Man Pun

In Proceedings of the IEEE International Conference on Bioinformatics and Biomedicine (BIBM), 2024

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Accurate prediction of mortality in nasopharyngeal carcinoma (NPC), a complex malignancy particularly challenging in advanced stages, is crucial for optimizing treatment strategies and improving patient outcomes. However, this predictive process is often compromised by the high-dimensional and heterogeneous nature of NPC-related data, coupled with the pervasive issue of incomplete multi-modal data, manifesting as missing radiological images or incomplete diagnostic reports. Traditional machine learning approaches suffer significant performance degradation when faced with such incomplete data, as they fail to effectively handle the high-dimensionality and intricate correlations across modalities. Even advanced multi-modal learning techniques like Transformers struggle to maintain robust performance in the presence of missing modalities, as they lack specialized mechanisms to adaptively integrate and align the diverse data types, while also capturing nuanced patterns and contextual relationships within the complex NPC data. To address these problem, we introduce IMAN: an adaptive network for robust NPC mortality prediction with missing modalities. IMAN features three integrated modules: the Dynamic Cross-Modal Calibration (DCMC) module employs adaptive, learnable parameters to scale and align medical images and field data; the Spatial-Contextual Attention Integration (SCAI) module enhances traditional Transformers by incorporating positional information within the self-attention mechanism, improving multi-modal feature integration; and the Context-Aware Feature Acquisition (CAFA) module adjusts convolution kernel positions through learnable offsets, allowing for adaptive feature capture across various scales and orientations in medical image modalities. Extensive experiments on our proprietary NPC dataset demonstrate IMAN’s robustness and high predictive accuracy, even with missing data. Compared to existing methods, IMAN consistently outperforms in scenarios with incomplete data, representing a significant advancement in mortality prediction for medical diagnostics and treatment planning. Our code is available at \urlhttps://github.com/king-huoye/BIBM-2024/tree/master.
@inproceedings{Huo:2024, title = {IMAN: An Adaptive Network for Robust NPC Mortality Prediction with Missing Modalities}, author = {Huo, Yejing and Huang, Guoheng and Cheng, Lianglun and He, Jianbin and Chen, Xuhang and Yuan, Xiaochen and Zhong, Guo and Pun, Chi-Man}, booktitle = {Proceedings of the IEEE International Conference on Bioinformatics and Biomedicine (BIBM)}, year = {2024}, pages = {2074-2079}, publisher = {IEEE}, address = {Lisbon, Portugal}, doi = {10.1109/BIBM62325.2024.10822792}, }
BIBM
FAQNet: Frequency-Aware Quaternion Network for Endoscopic Highlight Removal

Dingzhou Zhu , Guoheng Huang^†, Xiaochen Yuan , Xuhang Chen, Guo Zhong, Chi-Man Pun, and Jie Deng

In Proceedings of the IEEE International Conference on Bioinformatics and Biomedicine (BIBM), 2024

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Due to the built-in light source within the endoscope, the illumination of bodily mucous can cause the formation of highlight regions due to reflection. This not only interferes with the diagnosis conducted by doctors but also poses a challenge to subsequent computer vision tasks. To tackle this issue, we introduce FAQNet, a network specifically designed for endoscopic image highlight removal. FAQNet seamlessly integrates multi-channel information leveraging quaternion convolution and spatial channel attention within our Quaternion Multi-Channel Fusion (QMCF) Module. This allows it to capture intricate details of color, texture, spatial information, and highlight characteristics within the imaged organ. Additionally, by employing frequency domain transformation and dilated convolution, the Contextual Information Integration (CII) Module effectively enlarges the receptive field, organizing contextual information between highlight regions and their surrounding areas. Lastly, the PixelShuffle Upsampling (PSU) Module generates the restored image. We validate our model’s performance on two benchmark datasets, demonstrating its superiority over existing highlight removal methodologies.
@inproceedings{Zhu:2024, author = {Zhu, Dingzhou and Huang, Guoheng and Yuan, Xiaochen and Chen, Xuhang and Zhong, Guo and Pun, Chi-Man and Deng, Jie}, booktitle = {Proceedings of the IEEE International Conference on Bioinformatics and Biomedicine (BIBM)}, title = {FAQNet: Frequency-Aware Quaternion Network for Endoscopic Highlight Removal}, year = {2024}, pages = {1408-1413}, publisher = {IEEE}, doi = {10.1109/BIBM62325.2024.10822449}, address = {Lisbon, Portugal}, }
Test-Time Intensity Consistency Adaptation for Shadow Detection

Leyi Zhu, Weihuang Liu , Xinyi Chen , Zimeng Li , Xuhang Chen , Zhen Wang, and Chi-Man Pun^†

In International Conference on Neural Information Processing (ICONIP), 2024

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Shadow detection is crucial for accurate scene understanding in computer vision, yet it is challenged by the diverse appearances of shadows caused by variations in illumination, object geometry, and scene context. Deep learning models often struggle to generalize to real-world images due to the limited size and diversity of training datasets. To address this, we introduce TICA, a novel framework that leverages light-intensity information during test-time adaptation to enhance shadow detection accuracy. TICA exploits the inherent inconsistencies in light intensity across shadow regions to guide the model toward a more consistent prediction. A basic encoder-decoder model is initially trained on a labeled dataset for shadow detection. Then, during the testing phase, the network is adjusted for each test sample by enforcing consistent intensity predictions between two augmented input image versions. This consistency training specifically targets both foreground and background intersection regions to identify shadow regions within images accurately for robust adaptation. Extensive evaluations on the ISTD and SBU shadow detection datasets reveal that TICA significantly demonstrates that TICA outperforms existing state-of-the-art methods, achieving superior results in balanced error rate (BER).
@inproceedings{Zhu:2025, title = {Test-Time Intensity Consistency Adaptation for Shadow Detection}, author = {Zhu, Leyi and Liu, Weihuang and Chen, Xinyi and Li, Zimeng and Chen, Xuhang and Wang, Zhen and Pun, Chi-Man}, booktitle = {International Conference on Neural Information Processing (ICONIP)}, year = {2024}, publisher = {Springer}, address = {Auckland, New Zealand}, }
DocDeshadower: Frequency-Aware Transformer for Document Shadow Removal

Ziyang Zhou^*, Yingtie Lei^* , Xuhang Chen^† , Shenghong Luo , Wenjun Zhang, Chi-Man Pun^† , and Zhen Wang

In IEEE International Conference on Systems, Man, and Cybernetics (SMC), 2024

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Shadows in scanned documents pose significant challenges for document analysis and recognition tasks due to their negative impact on visual quality and readability. Current shadow removal techniques, including traditional methods and deep learning approaches, face limitations in handling varying shadow intensities and preserving document details. To address these issues, we propose DocDeshadower, a novel multi-frequency Transformer-based model built upon the Laplacian Pyramid. By decomposing the shadow image into multiple frequency bands and employing two critical modules: the Attention-Aggregation Network for low-frequency shadow removal and the Gated Multi-scale Fusion Transformer for global refinement. DocDeshadower effectively removes shadows at different scales while preserving document content. Extensive experiments demonstrate DocDe-shadower’s superior performance compared to state-of-the-art methods, highlighting its potential to significantly improve document shadow removal techniques. The code is available at https://github.com/leiyingtie/DocDeshadower.
@inproceedings{Zhou:2024, author = {Zhou, Ziyang and Lei, Yingtie and Chen, Xuhang and Luo, Shenghong and Zhang, Wenjun and Pun, Chi-Man and Wang, Zhen}, booktitle = {IEEE International Conference on Systems, Man, and Cybernetics (SMC)}, title = {DocDeshadower: Frequency-Aware Transformer for Document Shadow Removal}, year = {2024}, pages = {2468-2473}, doi = {10.1109/SMC54092.2024.10831480}, publisher = {IEEE}, address = {Kuching, Malaysia}, }
PDGC: Properly Disentangle by Gating and Contrasting for Cross-Domain Few-Shot Classification

Yanjie Chen , Guoheng Huang^†, Xiaochen Yuan , Xuhang Chen , Yan Li^† , Chi-man Pun, and Junbing Quan

In Computer Graphics International Conference (CGI), 2024

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A viable strategy for Cross-Domain Few-Shot Learning (CD-FSL) involves disentangling features into a domain-irrelevant part and a domain-specific part. The key to this strategy is how to make the model obtain more discriminative features in the target domain to keep accuracy and generalization in the few-shot setting. We propose the Properly Disentangle by Gating and Contrasting (PDGC) framework to accomplish this. It includes a Quaternion Gating Disentangle Module (QGDM) and an Attention-based Spatial Contrasting Module (ASCM). QGDM is utilized to delve deeper into the embedded inter-channel information and mitigate the inherent information loss during the disentangling process. Meanwhile, ASCM is utilized as a regularization constraint to avoid over-focusing on seen classes on CD-FSL problems leading to excessive disentangling and loss of generalization ability. Compared to the baseline, our method obtains an average of 2.3% and 3.68% improvement in 5-way 1-shot and 5-way 5-shot respectively in the FWT’s benchmark, and improves on most of the datasets in the BSCD-FSL’s benchmark.
@inproceedings{Chen:2027, title = {PDGC: Properly Disentangle by Gating and Contrasting for Cross-Domain Few-Shot Classification}, author = {Chen, Yanjie and Huang, Guoheng and Yuan, Xiaochen and Chen, Xuhang and Li, Yan and Pun, Chi-man and Quan, Junbing}, booktitle = {Computer Graphics International Conference (CGI)}, pages = {335--347}, year = {2024}, doi = {10.1007/978-3-031-82021-2_24}, publisher = {Springer}, address = {Geneva, Switzerland} }

2023

ICASSP
Shadocnet: Learning Spatial-Aware Tokens in Transformer for Document Shadow Removal

Xuhang Chen^*, Xiaodong Cun^*, Chi-Man Pun^†, and Shuqiang Wang^†

In Proceedings of the IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP), 2023

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Shadow removal improves the visual quality and legibility of digital copies of documents. However, document shadow removal remains an unresolved subject. Traditional techniques rely on heuristics that vary from situation to situation. Given the quality and quantity of current public datasets, the majority of neural network models are ill-equipped for this task. In this paper, we propose a Transformer-based model for document shadow removal that utilizes shadow context encoding and decoding in both shadow and shadow-free regions. Additionally, shadow detection and pixel-level enhancement are included in the whole coarse-to-fine process. On the basis of comprehensive benchmark evaluations, it is competitive with state-of-the-art methods.
@inproceedings{Chen:2023, author = {Chen, Xuhang and Cun, Xiaodong and Pun, Chi-Man and Wang, Shuqiang}, booktitle = {Proceedings of the IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP)}, pages = {1-5}, title = {Shadocnet: Learning Spatial-Aware Tokens in Transformer for Document Shadow Removal}, year = {2023}, publisher = {IEEE}, address = {Rhodes Island, Greece}, doi = {10.1109/ICASSP49357.2023.10095403}, }
IJCAI
A Large-Scale Film Style Dataset for Learning Multi-frequency Driven Film Enhancement

Zinuo Li^* , Xuhang Chen^*, Shuqiang Wang^†, and Chi-Man Pun^†

In Proceedings of the International Joint Conference on Artificial Intelligence (IJCAI), 2023

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Film, a classic image style, is culturally significant to the whole photographic industry since it marks the birth of photography. However, film photography is time-consuming and expensive, necessitating a more efficient method for collecting film-style photographs. Numerous datasets that have emerged in the field of image enhancement so far are not film-specific. In order to facilitate film-based image stylization research, we construct FilmSet, a large-scale and high-quality film style dataset. Our dataset includes three different film types and more than 5000 in-the-wild high resolution images. Inspired by the features of FilmSet images, we propose a novel framework called FilmNet based on Laplacian Pyramid for stylizing images across frequency bands and achieving film style outcomes. Experiments reveal that the performance of our model is superior than state-of-the-art techniques. The link of our dataset and code is https://github.com/CXH-Research/FilmNet.
@inproceedings{Li:2023, author = {Li, Zinuo and Chen, Xuhang and Wang, Shuqiang and Pun, Chi-Man}, booktitle = {Proceedings of the International Joint Conference on Artificial Intelligence (IJCAI)}, pages = {1160-1168}, title = {A Large-Scale Film Style Dataset for Learning Multi-frequency Driven Film Enhancement}, year = {2023}, publisher = {International Joint Conferences on Artificial Intelligence Organization}, address = {Macao, China}, doi = {10.24963/IJCAI.2023/129}, }
ICCV
High-Resolution Document Shadow Removal via A Large-Scale Real-World Dataset and A Frequency-Aware Shadow Erasing Net

Zinuo Li^* , Xuhang Chen^*, Chi-Man Pun^†, and Xiaodong Cun^†

In Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), 2023

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Shadows often occur when we capture the documents with casual equipment, which influences the visual quality and readability of the digital copies. Different from the algorithms for natural shadow removal, the algorithms in document shadow removal need to preserve the details of fonts and figures in high-resolution input. Previous works ignore this problem and remove the shadows via approximate attention and small datasets, which might not work in real-world situations. We handle high-resolution document shadow removal directly via a larger-scale real-world dataset and a carefully designed frequency-aware network. As for the dataset, we acquire over 7k couples of high-resolution (2462 x 3699) images of real-world document pairs with various samples under different lighting circumstances, which is 10 times larger than existing datasets. As for the design of the network, we decouple the high-resolution images in the frequency domain, where the low-frequency details and high-frequency boundaries can be effectively learned via the carefully designed network structure. Powered by our network and dataset, the proposed method clearly shows a better performance than previous methods in terms of visual quality and numerical results. The code, models, and dataset are available at https://github.com/CXH-Research/DocShadow-SD7K.
@inproceedings{Li:2024, author = {Li, Zinuo and Chen, Xuhang and Pun, Chi-Man and Cun, Xiaodong}, booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)}, pages = {12449-12458}, title = {High-Resolution Document Shadow Removal via A Large-Scale Real-World Dataset and A Frequency-Aware Shadow Erasing Net}, year = {2023}, publisher = {IEEE}, address = {Paris, France}, doi = {10.1109/ICCV51070.2023.01144}, }
PRCV
Brain Diffuser: An End-to-End Brain Image to Brain Network Pipeline

Xuhang Chen, Baiying Lei, Chi-Man Pun^†, and Shuqiang Wang^†

In Proceedings of the Chinese Conference on Pattern Recognition and Computer Vision (PRCV), 2023

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Brain network analysis is essential for diagnosing and intervention for Alzheimer’s disease (AD). However, previous research relied primarily on specific time-consuming and subjective toolkits. Only few tools can obtain the structural brain networks from brain diffusion tensor images (DTI). In this paper, we propose a diffusion based end-to-end brain network generative model Brain Diffuser that directly shapes the structural brain networks from DTI. Compared to existing toolkits, Brain Diffuser exploits more structural connectivity features and disease-related information by analyzing disparities in structural brain networks across subjects. For the case of Alzheimer’s disease, the proposed model performs better than the results from existing toolkits on the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database.
@inproceedings{Chen:2024, author = {Chen, Xuhang and Lei, Baiying and Pun, Chi-Man and Wang, Shuqiang}, booktitle = {Proceedings of the Chinese Conference on Pattern Recognition and Computer Vision (PRCV)}, pages = {16-26}, title = {Brain Diffuser: An End-to-End Brain Image to Brain Network Pipeline}, year = {2023}, publisher = {Springer}, address = {Xiamen, China}, doi = {10.1007/978-981-99-8558-6_2}, }
MR Image Super-Resolution Using Wavelet Diffusion for Predicting Alzheimer’s Disease

Guoli Huang , Xuhang Chen, Yanyan Shen, and Shuqiang Wang^†

In International Conference on Brain Informatics (BI), 2023

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Alzheimer’s disease (AD) is a neurodegenerative disorder that exerts a substantial influence on individuals worldwide. Magnetic resonance imaging (MRI) can detect and track disease progression. However, the majority of MRI data currently available is characterized by low resolution. The present study introduces a novel approach for MRI super-resolution by integrating diffusion model with wavelet decomposition techniques. The methodology proposed in this study is tailored to address the issue of restricted data availability. It utilizes adversarial training and capitalizes on the advantages of denoising diffusion probabilistic model (DDPM), while simultaneously avoiding the problem of diversity collapse. The proposed method incorporates wavelet decomposition within the latent space to augment the resilience and efficiency of generative models. The experimental findings demonstrate the superior efficacy of our proposed model in contrast to alternative techniques, as indicated by the SSIM and FID metrics. Moreover, our methodology has the potential to enhance the precision of Alzheimer’s disease assessment.
@inproceedings{Huang:2023, author = {Huang, Guoli and Chen, Xuhang and Shen, Yanyan and Wang, Shuqiang}, booktitle = {International Conference on Brain Informatics (BI)}, pages = {146-157}, title = {MR Image Super-Resolution Using Wavelet Diffusion for Predicting Alzheimer's Disease}, year = {2023}, doi = {10.1007/978-3-031-43075-6_13}, publisher = {Springer}, address = {Hoboken, NJ, USA}, }
Generative AI Enables EEG Data Augmentation for Alzheimer’s Disease Detection Via Diffusion Model

Tong Zhou , Xuhang Chen, Yanyan Shen, Martin Nieuwoudt, Chi-Man Pun, and Shuqiang Wang^†

In IEEE International Symposium on Product Compliance Engineering-Asia (ISPCE-ASIA), 2023

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Electroencephalography (EEG) is a non-invasive method to measure the electrical activity of the brain and can be regarded as an effective means of diagnosing Alzheimer’s disease (AD). However, The diagnosis of AD based on EEG often encounters the problem of data scarcity. For deep learning models, data scarcity problems lead to overfitting of the model, making it impossible to build effective, highly accurate, and stable models. Data augmentation is often used as an effective means to solve the problem of data scarcity. In this paper, we propose a diffusion models-based EEG data augmentation framework (Diff-EEG). The proposed framework Vector Quantized Variational autoencoders(VQ-VAE) and guided diffusion models to learn the distribution of limited EEG dataset, thereby generating high-quality artificially synthesized EEG data to supplement the dataset. Finally, the result of experiments demonstrate that our proposed method can generate high-quality artificial EEG data, which can effectively improve the performance of AD diagnosis models.
@inproceedings{Zhou:2023, title = {Generative AI Enables EEG Data Augmentation for Alzheimer's Disease Detection Via Diffusion Model}, author = {Zhou, Tong and Chen, Xuhang and Shen, Yanyan and Nieuwoudt, Martin and Pun, Chi-Man and Wang, Shuqiang}, booktitle = {IEEE International Symposium on Product Compliance Engineering-Asia (ISPCE-ASIA)}, pages = {1-6}, year = {2023}, publisher = {IEEE}, doi = {10.1109/ISPCE-ASIA60405.2023.10365931}, address = {Shanghai, China}, }

Patents

2024

一种端到端的轻量级可见光图像到红外图像转换方法

Xiangxin Zhou , Yijia Chen , Xuhang Chen , Pinghua Chen, and Chaocheng Shao

2024

Bib PDF

@patent{Zhou:2025,
  title = {一种端到端的轻量级可见光图像到红外图像转换方法},
  author = {Zhou, Xiangxin and Chen, Yijia and Chen, Xuhang and Chen, Pinghua and Shao, Chaocheng},
  year = {2024},
  address = {510062 广东省广州市东风东路729号},
  pages = {12},
  edition = {CN118887484A},
}

基于多模态计算的脑疾病早期智能分级筛查系统

Shuqiang Wang, Changhong Jing , Xuhang Chen, and Yong Hu

2024

Bib PDF

@patent{Wang:2026,
  title = {基于多模态计算的脑疾病早期智能分级筛查系统},
  author = {Wang, Shuqiang and Jing, Changhong and Chen, Xuhang and Hu, Yong},
  year = {2024},
  address = {518055 广东省深圳市南山区深圳大学城学苑大道1068号},
  pages = {29},
  edition = {CN118887484A},
}

2023

多模态脑网络计算方法、装置、设备及存储介质

Shuqiang Wang, Qiankun Zuo , Xuhang Chen, and Chi-Man Pun

2023

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@patent{Wang:2023,
  author = {Wang, Shuqiang and Zuo, Qiankun and Chen, Xuhang and Pun, Chi-Man},
  title = {多模态脑网络计算方法、装置、设备及存储介质},
  edition = {CN115775626A},
  year = {2023},
  pages = {21},
  address = {518055 广东省深圳市南山区西丽大学城学苑大道1068号},
}

结构功能关联的多模态脑网络计算方法、装置、设备及介质

Shuqiang Wang, Junren Pan, Chi-Man Pun , and Xuhang Chen

2023

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@patent{Wang:2024,
  author = {Wang, Shuqiang and Pan, Junren and Pun, Chi-Man and Chen, Xuhang},
  title = {结构功能关联的多模态脑网络计算方法、装置、设备及介质},
  edition = {CN115813367A},
  year = {2023},
  pages = {19},
  address = {518055 广东省深圳市南山区西丽大学城学苑大道1068号},
}

知识和数据驱动的脑网络计算方法、装置、电子设备及存储介质

Shuqiang Wang, Heng Kong , Xuhang Chen, and Chi-Man Pun

2023

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@patent{Wang:2025,
  author = {Wang, Shuqiang and Kong, Heng and Chen, Xuhang and Pun, Chi-Man},
  title = {知识和数据驱动的脑网络计算方法、装置、电子设备及存储介质},
  edition = {CN116188366A},
  year = {2023},
  pages = {20},
  address = {518000 广东省深圳市南山区西丽深圳大学城学苑大道1068号},
}

2021

一种高效稳定的双头转动轴

Sixin Luo, Zhongliang Luo , Xuhang Chen, and Jianfeng Huang

2021

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@patent{Luo:2021,
  author = {Luo, Sixin and Luo, Zhongliang and Chen, Xuhang and Huang, Jianfeng},
  title = {一种高效稳定的双头转动轴},
  edition = {CN212616161U},
  year = {2021},
  pages = {7},
  address = {516000 广东省惠州市仲恺高新区仲恺大道666号同方信息港23楼(仲恺信息学院)},
}

一种精密仪器用的导向轴

Sixin Luo , Xuhang Chen, Zhongliang Luo, and Jianfeng Huang

2021

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@patent{Luo:2022,
  author = {Luo, Sixin and Chen, Xuhang and Luo, Zhongliang and Huang, Jianfeng},
  title = {一种精密仪器用的导向轴},
  edition = {CN212959484U},
  year = {2021},
  pages = {6},
  address = {516000 广东省惠州市仲恺高新区仲恺大道666号同方信息港23楼(仲恺信息学院)},
}

一种便于存放的滚珠丝杆组件

Sixin Luo, Zhongliang Luo , Xuhang Chen, and Jianfeng Huang

2021

Bib PDF

@patent{Luo:2023,
  author = {Luo, Sixin and Luo, Zhongliang and Chen, Xuhang and Huang, Jianfeng},
  title = {一种便于存放的滚珠丝杆组件},
  edition = {CN213332260U},
  year = {2021},
  pages = {8},
  address = {516000 广东省惠州市仲恺高新区仲恺大道666号同方信息港23楼(仲恺信息学院)},
}

一种用于音箱的弧状角结构的制作方法

Sixin Luo, Zhongliang Luo, Jianfeng Huang , and Xuhang Chen

2021

Bib PDF

@patent{Luo:2025,
  author = {Luo, Sixin and Luo, Zhongliang and Huang, Jianfeng and Chen, Xuhang},
  title = {一种用于音箱的弧状角结构的制作方法},
  edition = {CN113301492A},
  year = {2021},
  pages = {7},
  address = {516000 广东省惠州市仲恺高新区仲恺大道666号同方信息港23楼(仲恺信息学院)},
}

Journal Articles

2025

HEDN: multi-oriented hierarchical extraction and dual-frequency decoupling network for 3D medical image segmentation

Yu Wang , Guoheng Huang^†, Zeng Lu , Ying Wang , Xuhang Chen, Xiaochen Yuan , Yan Li^†, Liujie Ni^† , and Yingping Huang^†

Medical & Biological Engineering & Computing, 2025

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Previous 3D encoder-decoder segmentation architectures struggled with fine-grained feature decomposition, resulting in unclear feature hierarchies when fused across layers. Furthermore, the blurred nature of contour boundaries in medical imaging limits the focus on high-frequency contour features. To address these challenges, we propose a Multi-oriented Hierarchical Extraction and Dual-frequency Decoupling Network (HEDN), which consists of three modules: Encoder-Decoder Module (E-DM), Multi-oriented Hierarchical Extraction Module (Multi-HEM), and Dual-frequency Decoupling Module (Dual-DM). The E-DM performs the basic encoding and decoding tasks, while Multi-HEM decomposes and fuses spatial and slice-level features in 3D, enriching the feature hierarchy by weighting them through 3D fusion. Dual-DM separates high-frequency features from the reconstructed network using self-supervision. Finally, the self-supervised high-frequency features separated by Dual-DM are inserted into the process following Multi-HEM, enhancing interactions and complementarities between contour features and hierarchical features, thereby mutually reinforcing both aspects. On the Synapse dataset, HEDN outperforms existing methods, boosting Dice Similarity Score (DSC) by 1.38% and decreasing 95% Hausdorff Distance (HD95) by 1.03 mm. Likewise, on the Automatic Cardiac Diagnosis Challenge (ACDC) dataset, HEDN achieves 0.5% performance gains across all categories.
@article{Wang:2027, author = {Wang, Yu and Huang, Guoheng and Lu, Zeng and Wang, Ying and Chen, Xuhang and Yuan, Xiaochen and Li, Yan and Ni, Liujie and Huang, Yingping}, title = {HEDN: multi-oriented hierarchical extraction and dual-frequency decoupling network for 3D medical image segmentation}, journal = {Medical {\&} Biological Engineering {\&} Computing}, year = {2025}, volume = {63}, number = {1}, pages = {267--291}, publisher = {Springer}, doi = {10.1007/s11517-024-03192-y}, }
A comprehensive survey on shadow removal from document images: datasets, methods, and opportunities

Bingshu Wang , Changping Li^†, Wenbin Zou , Yongjun Zhang , Xuhang Chen , and C.l. Philip Chen

Vicinagearth, 2025

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With the rapid development of document digitization, people have become accustomed to capturing and processing documents using electronic devices such as smartphones. However, the captured document images often suffer from issues like shadows and noise due to environmental factors, which can affect their readability. To improve the quality of captured document images, researchers have proposed a series of models or frameworks and applied them in distinct scenarios such as image enhancement, and document information extraction. In this paper, we primarily focus on shadow removal methods and open-source datasets. We concentrate on recent advancements in this area, first organizing and analyzing nine available datasets. Then, the methods are categorized into conventional methods and neural network-based methods. Conventional methods use manually designed features and include shadow map-based approaches and illumination-based approaches. Neural network-based methods automatically generate features from data and are divided into single-stage approaches and multi-stage approaches. We detail representative algorithms and briefly describe some typical techniques. Finally, we analyze and discuss experimental results, identifying the limitations of datasets and methods. Future research directions are discussed, and nine suggestions for shadow removal from document images are proposed. To our knowledge, this is the first survey of shadow removal methods and related datasets from document images.
@article{Wang:2028, author = {Wang, Bingshu and Li, Changping and Zou, Wenbin and Zhang, Yongjun and Chen, Xuhang and Chen, C.l. Philip}, title = {A comprehensive survey on shadow removal from document images: datasets, methods, and opportunities}, journal = {Vicinagearth}, publisher = {Springer}, year = {2025}, volume = {2}, number = {1}, doi = {10.1007/s44336-024-00010-9}, }
Qean: quaternion-enhanced attention network for visual dance generation

Zhizhen Zhou, Yejing Huo , Guoheng Huang^†, An Zeng , Xuhang Chen^† , Lian Huang, and Zinuo Li

The Visual Computer, 2025

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The study of music-generated dance is a novel and challenging image generation task. It aims to input a piece of music and seed motions, then generate natural dance movements for the subsequent music. Transformer-based methods face challenges in time-series prediction tasks related to human movements and music due to their struggle in capturing the nonlinear relationship and temporal aspects. This can lead to issues like joint deformation, role deviation, floating, and inconsistencies in dance movements generated in response to the music. In this paper, we propose a quaternion-enhanced attention network for visual dance synthesis from a quaternion perspective, which consists of a spin position embedding (SPE) module and a quaternion rotary attention (QRA) module. First, SPE embeds position information into self-attention in a rotational manner, leading to better learning of features of movement sequences and audio sequences and improved understanding of the connection between music and dance. Second, QRA represents and fuses 3D motion features and audio features in the form of a series of quaternions, enabling the model to better learn the temporal coordination of music and dance under the complex temporal cycle conditions of dance generation. Finally, we conducted experiments on the dataset AIST++, and the results show that our approach achieves better and more robust performance in generating accurate, high-quality dance movements. Our source code and dataset can be available from https://github.com/MarasyZZ/QEAN and https://google.github.io/aistplusplus_dataset, respectively.
@article{Zhou:2026, title = {Qean: quaternion-enhanced attention network for visual dance generation}, author = {Zhou, Zhizhen and Huo, Yejing and Huang, Guoheng and Zeng, An and Chen, Xuhang and Huang, Lian and Li, Zinuo}, journal = {The Visual Computer}, volume = {41}, number = {2}, pages = {961--973}, year = {2025}, doi = {10.1007/s00371-024-03376-5}, publisher = {Springer}, }
Weakly supervised semantic segmentation via saliency perception with uncertainty-guided noise suppression

Xinyi Liu , Guoheng Huang^†, Xiaochen Yuan^† , Zewen Zheng, Guo Zhong , Xuhang Chen, and Chi-Man Pun

The Visual Computer, 2025

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Weakly Supervised Semantic Segmentation (WSSS) has become increasingly popular for achieving remarkable segmentation with only image-level labels. Current WSSS approaches extract Class Activation Mapping (CAM) from classification models to produce pseudo-masks for segmentation supervision. However, due to the gap between image-level supervised classification loss and pixel-level CAM generation tasks, the model tends to activate discriminative regions at the image level rather than pursuing pixel-level classification results. Moreover, insufficient supervision leads to unrestricted attention diffusion in the model, further introducing inter-class recognition noise. In this paper, we introduce a framework that employs Saliency Perception and Uncertainty, which includes a Saliency Perception Module (SPM) with Pixel-wise Transfer Loss (SP-PT), and an Uncertainty-guided Noise Suppression method. Specifically, within the SPM, we employ a hybrid attention mechanism to expand the receptive field of the module and enhance its ability to perceive salient object features. Meanwhile, a Pixel-wise Transfer Loss is designed to guide the attention diffusion of the classification model to non-discriminative regions at the pixel-level, thereby mitigating the bias of the model. To further enhance the robustness of CAM for obtaining more accurate pseudo-masks, we propose a noise suppression method based on uncertainty estimation, which applies a confidence matrix to the loss function to suppress the propagation of erroneous information and correct it, thus making the model more robust to noise. We conducted experiments on the PASCAL VOC 2012 and MS COCO 2014, and the experimental results demonstrate the effectiveness of our proposed framework. Code is available at https://github.com/pur-suit/SPU.
@article{Liu:2025, author = {Liu, Xinyi and Huang, Guoheng and Yuan, Xiaochen and Zheng, Zewen and Zhong, Guo and Chen, Xuhang and Pun, Chi-Man}, title = {Weakly supervised semantic segmentation via saliency perception with uncertainty-guided noise suppression}, journal = {The Visual Computer}, volume = {41}, number = {4}, pages = {2891--2906}, year = {2025}, doi = {10.1007/S00371-024-03574-1}, publisher = {Springer}, }
Psanet: prototype-guided salient attention for few-shot segmentation

Hao Li , Guoheng Huang^†, Xiaochen Yuan , Zewen Zheng , Xuhang Chen, Guo Zhong^†, and Chi-Man Pun

The Visual Computer, 2025

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Few-shot semantic segmentation aims to learn a generalized model for unseen-class segmentation with just a few densely annotated samples. Most current metric-based prototype learning models utilize prototypes to assist in query sample segmentation by directly utilizing support samples through Masked Average Pooling. However, these methods frequently fail to consider the semantic ambiguity of prototypes, the limitations in performance when dealing with extreme variations in objects, and the semantic similarities between different classes. In this paper, we introduce a novel network architecture named Prototype-guided Salient Attention Network (PSANet). Specifically, we employ prototype-guided attention to learn salient regions, allocating different attention weights to features at different spatial locations of the target to enhance the significance of salient regions within the prototype. In order to mitigate the impact of external distractor categories on the prototype, our proposed contrastive loss has the capability to acquire a more discriminative prototype to promote inter-class feature separation and intra-class feature compactness. Moreover, we suggest implementing a refinement operation for the multi-scale module in order to enhance the ability to capture complete contextual information regarding features at various scales. The effectiveness of our strategy is demonstrated by extensive tests performed on the PASCAL-5i and COCO-20i datasets, despite its inherent simplicity. Our code is available at https://github.com/woaixuexixuexi/PSANet.
@article{Li:2028, title = {Psanet: prototype-guided salient attention for few-shot segmentation}, author = {Li, Hao and Huang, Guoheng and Yuan, Xiaochen and Zheng, Zewen and Chen, Xuhang and Zhong, Guo and Pun, Chi-Man}, journal = {The Visual Computer}, volume = {41}, number = {4}, pages = {2987--3001}, year = {2025}, publisher = {Springer}, doi = {10.1007/S00371-024-03582-1}, }
GRE2-MDCL: Graph Representation Embedding Enhanced via Multidimensional Contrastive Learning

Quanjun Li , Weixuan Li , Xiya Zheng, Junhua Zhou , Wenming Zhong , Xuhang Chen, and Chao Long^†

IEEE Access, 2025

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Graph representation learning aims to preserve graph topology when mapping nodes to vector representations, enabling downstream tasks like node classification and community detection. However, most graph neural network models require extensive labelled data, limiting their practical applicability. To address this, researchers have explored Graph Contrastive Learning (GCL), which uses enhanced graph data and contrastive learning to better capture graph structure and features, providing new avenues for solving real-world problems with limited labelled data. Building on this, this work proposes Graph Representation Embedding Enhanced via Multidimensional Contrastive Learning (GRE2-MDCL). GRE2-MDCL first globally and locally augments the input graph using SVD and LAGNN. The enhanced data is then fed into a triple network with a multi-head attention GNN as the core model. Finally, GRE2-MDCL constructs a multidimensional contrastive loss, incorporating cross-network, cross-view, and neighbor contrast, to optimize the model. Evaluated on Cora, Citeseer, and PubMed, GRE2-MDCL achieves average accuracies of 83.1%, 72.6%, and 82.7%, outperforming baseline GCL models. Visualizations also show tighter intra-cluster aggregation and clearer inter-cluster boundaries, demonstrating the framework’s effectiveness in improving upon the baseline.
@article{Li:2029, author = {Li, Quanjun and Li, Weixuan and Zheng, Xiya and Zhou, Junhua and Zhong, Wenming and Chen, Xuhang and Long, Chao}, journal = {IEEE Access}, title = {GRE2-MDCL: Graph Representation Embedding Enhanced via Multidimensional Contrastive Learning}, year = {2025}, volume = {13}, pages = {61312-61321}, doi = {10.1109/ACCESS.2025.3553862}, publisher = {IEEE} }
TCSVT
Underwater Image Restoration Through a Prior Guided Hybrid Sense Approach and Extensive Benchmark Analysis

Xiaojiao Guo^* , Xuhang Chen^*, Shuqiang Wang^†, and Chi-Man Pun^†

IEEE Transactions on Circuits and Systems for Video Technology, 2025

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Underwater imaging grapples with challenges from light-water interactions, leading to color distortions and reduced clarity. In response to these challenges, we propose a novel Color Balance Prior Guided Hybrid Sense Underwater Image Restoration framework (GuidedHybSensUIR). This framework operates on multiple scales, employing the proposed Detail Restorer module to restore low-level detailed features at finer scales and utilizing the proposed Feature Contextualizer module to capture long-range contextual relations of high-level general features at a broader scale. The hybridization of these different scales of sensing results effectively addresses color casts and restores blurry details. In order to effectively point out the evolutionary direction for the model, we propose a novel Color Balance Prior as a strong guide in the feature contextualization step and as a weak guide in the final decoding phase. We construct a comprehensive benchmark using paired training data from three real-world underwater datasets and evaluate on six test sets, including three paired and three unpaired, sourced from four real-world underwater datasets. Subsequently, we tested 14 traditional and retrained 23 deep learning existing underwater image restoration methods on this benchmark, obtaining metric results for each approach. This effort aims to furnish a valuable benchmarking dataset for standard basis for comparison. The extensive experiment results demonstrate that our method outperforms 37 other state-of-the-art methods overall on various benchmark datasets and metrics, despite not achieving the best results in certain individual cases. The code and dataset are available at https://github.com/CXH-Research/GuidedHybSensUIR.
@article{Guo:2026, author = {Guo, Xiaojiao and Chen, Xuhang and Wang, Shuqiang and Pun, Chi-Man}, journal = {IEEE Transactions on Circuits and Systems for Video Technology}, title = {Underwater Image Restoration Through a Prior Guided Hybrid Sense Approach and Extensive Benchmark Analysis}, year = {2025}, volume = {35}, number = {5}, pages = {4784-4800}, doi = {10.1109/TCSVT.2025.3525593}, publisher = {IEEE}, }
TCIA: A Transformer-CNN Model With Illumination Adaptation for Enhancing Cell Image Saliency and Contrast

Jietao Yang , Guoheng Huang^† , Yanzhang Luo, Xiaofeng Zhang, Xiaochen Yuan , Xuhang Chen, Chi-Man Pun, and Mu-yan Cai^†

IEEE Transactions on Instrumentation and Measurement, 2025

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Inconsistent illumination across imaging instruments poses significant challenges for accurate cell detection and analysis. Conventional methods (e.g., histogram equalization and basic filtering) struggle to adapt to complex lighting conditions, resulting in limited image enhancement and inconsistent performance. To address these issues, we propose the transformer-convolutional neural network (CNN) model with illumination adaptation (TCIA) model, which improves cell image saliency and contrast. By extracting illumination invariant features (IIF) using a locally sensitive histogram as prior knowledge, our model effectively adapts to varying illumination conditions. The TCIA framework employs hybrid convolution blocks (HCBs) to extract and preserve essential features from image pairs, followed by a two-branch decomposition-fusion network that separates features into low-frequency and high-frequency components. The Lite-Transformer (LT) captures global context for low-frequency features, while the circular difference invertible (CDI) module focuses on fine-grained textures and edges for high-frequency features. These features are then fused and reconstructed to produce high-contrast, salient images. Extensive experiments on three datasets (MoNuSeg, MoNuSAC, and our contributed MTGC) demonstrate that TCIA outperforms existing methods in image fusion and cell detection, achieving an average improvement in detection accuracy of 2%. This work provides a robust and innovative solution for enhanced cell imaging, contributing to more precise diagnostics and analysis. The source code will be available at https://github.com/Mrzhans/TCIA.
@article{Yang:2025, author = {Yang, Jietao and Huang, Guoheng and Luo, Yanzhang and Zhang, Xiaofeng and Yuan, Xiaochen and Chen, Xuhang and Pun, Chi-Man and Cai, Mu-yan}, journal = {IEEE Transactions on Instrumentation and Measurement}, title = {TCIA: A Transformer-CNN Model With Illumination Adaptation for Enhancing Cell Image Saliency and Contrast}, year = {2025}, volume = {74}, pages = {1-15}, doi = {10.1109/TIM.2025.3527542}, publisher = {IEEE}, }
NiDNeXt: Cross-Channel Interactive And Multi-Scale Fusion for Nighttime Dehazing

Yuxuan Lai , Guoheng Huang^†, Lianglun Cheng , Xuhang Chen, Xiaochen Yuan, Guo Zhong, and Chi-Man Pun

Measurement Science and Technology, 2025

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Due to severe haze pollution and color attenuation, nighttime dehazing is an extremely challenging problem. Current research often neglects the impact of multiple unevenly illuminated active color light sources in night scenes, which may lead to halo, mist, glow and noise with localized, coupled, inconsistent frequency and unnatural characteristics. Additionally, after fog removal, night images may suffer from color distortion, detail loss, and texture blur. To address these issues, we present NiDNeXt, a nighttime dehazing network that integrates Multi-Scale Progressive Fusion (MSPF), Cross-Channel Interactive Attention (CCIA), and Fourier Dynamic Frequency Filter (FDFF) modules. MSPF fuses spatial and frequency information locally, removing haze, glow, and noise in a coarse-to-fine manner across multiple scales while regenerating image details. CCIA allows channels to selectively absorb valuable information from adjacent channels using learned dynamic weights, enhancing the extraction of clear elements, color channel compensation, and texture restoration. FDFF, utilized within MSPF and CCIA, exploits dual-domain difference information to improve haze removal, glow elimination, texture restoration, and color correction. Our experiments on public benchmark datasets confirm the effectiveness and superiority of NiDNeXt over state-of-the-art methods.
@article{Lai:2025, author = {Lai, Yuxuan and Huang, Guoheng and Cheng, Lianglun and Chen, Xuhang and Yuan, Xiaochen and Zhong, Guo and Pun, Chi-Man}, title = {NiDNeXt: Cross-Channel Interactive And Multi-Scale Fusion for Nighttime Dehazing}, journal = {Measurement Science and Technology}, year = {2025}, doi = {10.1088/1361-6501/adea8d}, publisher = {IOP Publishing}, }

2024

WavEnhancer: Unifying Wavelet and Transformer for Image Enhancement

Zinuo Li^* , Xuhang Chen^*, Shuna Guo, Shuqiang Wang^†, and Chi-Man Pun^†

Journal of Computer Science and Technology, 2024

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Image enhancement is a widely used technique in digital image processing that aims to improve image aesthetics and visual quality. However, traditional methods of enhancement based on pixel-level or global-level modifications have limited effectiveness. Recently, as learning-based techniques gain popularity, various studies are now focusing on utilizing networks for image enhancement. However, these techniques often fail to optimize image frequency domains. This study addresses this gap by introducing a transformer-based model for improving images in the wavelet domain. The proposed model refines various frequency bands of an image and prioritizes local details and high-level features. Consequently, the proposed technique produces superior enhancement results. The proposed model’s performance was assessed through comprehensive benchmark evaluations, and the results suggest it outperforms the state-of-the-art techniques.
@article{Li:2026, title = {WavEnhancer: Unifying Wavelet and Transformer for Image Enhancement}, author = {Li, Zinuo and Chen, Xuhang and Guo, Shuna and Wang, Shuqiang and Pun, Chi-Man}, journal = {Journal of Computer Science and Technology}, volume = {39}, number = {2}, pages = {336-345}, year = {2024}, publisher = {Springer}, doi = {10.1007/s11390-024-3414-z}, }
U-shaped convolutional transformer GAN with multi-resolution consistency loss for restoring brain functional time-series and dementia diagnosis

Qiankun Zuo , Ruiheng Li, Binghua Shi, Jin Hong, Yanfei Zhu , Xuhang Chen, Yixian Wu, and Jia Guo^†

Frontiers in Computational Neuroscience, 2024

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The blood oxygen level-dependent (BOLD) signal derived from functional neuroimaging is commonly used in brain network analysis and dementia diagnosis. Missing the BOLD signal may lead to bad performance and misinterpretation of findings when analyzing neurological disease. Few studies have focused on the restoration of brain functional time-series data.
@article{Zuo:2024, title = {U-shaped convolutional transformer GAN with multi-resolution consistency loss for restoring brain functional time-series and dementia diagnosis}, author = {Zuo, Qiankun and Li, Ruiheng and Shi, Binghua and Hong, Jin and Zhu, Yanfei and Chen, Xuhang and Wu, Yixian and Guo, Jia}, journal = {Frontiers in Computational Neuroscience}, volume = {18}, pages = {1387004}, year = {2024}, publisher = {Frontiers Media SA}, doi = {10.3389/fncom.2024.1387004}, }
RM-UNet: UNet-like Mamba with rotational SSM module for medical image segmentation

Hao Tang , Guoheng Huang^†, Lianglun Cheng, Xiaochen Yuan, Qi Tao , Xuhang Chen, Guo Zhong^†, and Xiaohui Yang^†

Signal, Image and Video Processing, 2024

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Accurate segmentation of tissues and lesions is crucial for disease diagnosis, treatment planning, and surgical navigation. Yet, the complexity of medical images presents significant challenges for traditional Convolutional Neural Networks and Transformer models due to their limited receptive fields or high computational complexity. State Space Models (SSMs) have recently shown notable vision performance, particularly Mamba and its variants. However, their feature extraction methods may not be sufficiently effective and retain some redundant structures, leaving room for parameter reduction. In response to these challenges, we introduce a methodology called Rotational Mamba-UNet, characterized by Residual Visual State Space (ResVSS) block and Rotational SSM Module. The ResVSS block is devised to mitigate network degradation caused by the diminishing efficacy of information transfer from shallower to deeper layers. Meanwhile, the Rotational SSM Module is devised to tackle the challenges associated with channel feature extraction within State Space Models. Finally, we propose a weighted multi-level loss function, which fully leverages the outputs of the decoder’s three stages for supervision. We conducted experiments on ISIC17, ISIC18, CVC-300, Kvasir-SEG, CVC-ColonDB, Kvasir-Instrument datasets, and Low-grade Squamous Intraepithelial Lesion datasets provided by The Third Affiliated Hospital of Sun Yat-sen University, demonstrating the superior segmentation performance of our proposed RM-UNet. Additionally, compared to the previous VM-UNet, our model achieves a one-third reduction in parameters. Our code is available at https://github.com/Halo2Tang/RM-UNet.
@article{Tang:2024, author = {Tang, Hao and Huang, Guoheng and Cheng, Lianglun and Yuan, Xiaochen and Tao, Qi and Chen, Xuhang and Zhong, Guo and Yang, Xiaohui}, title = {RM-UNet: UNet-like Mamba with rotational SSM module for medical image segmentation}, journal = {Signal, Image and Video Processing}, year = {2024}, volume = {18}, number = {11}, pages = {8427-8443}, publisher = {Springer}, doi = {10.1007/s11760-024-03484-8}, }
Cross-domain visual prompting with spatial proximity knowledge distillation for histological image classification

Xiaohong Li , Guoheng Huang^†, Lianglun Cheng, Guo Zhong^†, Weihuang Liu , Xuhang Chen, and Muyan Cai^†

Journal of Biomedical Informatics, 2024

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Histological classification is a challenging task due to the diverse appearances, unpredictable variations, and blurry edges of histological tissues. Recently, many approaches based on large networks have achieved satisfactory performance. However, most of these methods rely heavily on substantial computational resources and large high-quality datasets, limiting their practical application. Knowledge Distillation (KD) offers a promising solution by enabling smaller networks to achieve performance comparable to that of larger networks. Nonetheless, KD is hindered by the problem of high-dimensional characteristics, which makes it difficult to capture tiny scattered features and often leads to the loss of edge feature relationships.
@article{Li:2027, title = {Cross-domain visual prompting with spatial proximity knowledge distillation for histological image classification}, journal = {Journal of Biomedical Informatics}, volume = {158}, pages = {104728}, year = {2024}, doi = {10.1016/j.jbi.2024.104728}, author = {Li, Xiaohong and Huang, Guoheng and Cheng, Lianglun and Zhong, Guo and Liu, Weihuang and Chen, Xuhang and Cai, Muyan}, publisher = {Elsevier} }
MFDNet: Multi-Frequency Deflare Network for efficient nighttime flare removal

Yiguo Jiang , Xuhang Chen, Chi-Man Pun^†, Shuqiang Wang, and Wei Feng

The Visual Computer, 2024

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When light is scattered or reflected accidentally in the lens, flare artifacts may appear in the captured photographs, affecting the photographs’ visual quality. The main challenge in flare removal is to eliminate various flare artifacts while preserving the original content of the image. To address this challenge, we propose a lightweight Multi-Frequency Deflare Network (MFDNet) based on the Laplacian Pyramid. Our network decomposes the flare-corrupted image into low- and high-frequency bands, effectively separating the illumination and content information in the image. The low-frequency part typically contains illumination information, while the high-frequency part contains detailed content information. So our MFDNet consists of two main modules: the Low-Frequency Flare Perception Module (LFFPM) to remove flare in the low-frequency part and the Hierarchical Fusion Reconstruction Module (HFRM) to reconstruct the flare-free image. Specifically, to perceive flare from a global perspective while retaining detailed information for image restoration, LFFPM utilizes Transformer to extract global information while utilizing a convolutional neural network to capture detailed local features. Then HFRM gradually fuses the outputs of LFFPM with the high-frequency component of the image through feature aggregation. Moreover, our MFDNet can reduce the computational cost by processing in multiple frequency bands instead of directly removing the flare on the input image. Experimental results demonstrate that our approach outperforms state-of-the-art methods in removing nighttime flare on real-world and synthetic images from the Flare7K dataset. Furthermore, the computational complexity of our model is remarkably low.
@article{Jiang:2025, author = {Jiang, Yiguo and Chen, Xuhang and Pun, Chi-Man and Wang, Shuqiang and Feng, Wei}, title = {MFDNet: Multi-Frequency Deflare Network for efficient nighttime flare removal}, journal = {The Visual Computer}, volume = {40}, number = {11}, pages = {7575--7588}, year = {2024}, publisher = {Springer}, doi = {10.1007/S00371-024-03540-X}, }

2023

Generative AI for brain image computing and brain network computing: a review

Changwei Gong, Changhong Jing , Xuhang Chen, Chi-Man Pun , Guoli Huang, Ashirbani Saha, Martin Nieuwoudt , Hanxiong Li, Yong Hu^†, and Shuqiang Wang^†

Frontiers in Neuroscience, 2023

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Recent years have witnessed a significant advancement in brain imaging techniques that offer a non-invasive approach to mapping the structure and function of the brain. Concurrently, generative artificial intelligence (AI) has experienced substantial growth, involving using existing data to create new content with a similar underlying pattern to real-world data. The integration of these two domains, generative AI in neuroimaging, presents a promising avenue for exploring various fields of brain imaging and brain network computing, particularly in the areas of extracting spatiotemporal brain features and reconstructing the topological connectivity of brain networks. Therefore, this study reviewed the advanced models, tasks, challenges, and prospects of brain imaging and brain network computing techniques and intends to provide a comprehensive picture of current generative AI techniques in brain imaging. This review is focused on novel methodological approaches and applications of related new methods. It discussed fundamental theories and algorithms of four classic generative models and provided a systematic survey and categorization of tasks, including co-registration, super-resolution, enhancement, classification, segmentation, cross-modality, brain network analysis, and brain decoding. This paper also highlighted the challenges and future directions of the latest work with the expectation that future research can be beneficial.
@article{Gong:2023, title = {Generative AI for brain image computing and brain network computing: a review}, author = {Gong, Changwei and Jing, Changhong and Chen, Xuhang and Pun, Chi-Man and Huang, Guoli and Saha, Ashirbani and Nieuwoudt, Martin and Li, Hanxiong and Hu, Yong and Wang, Shuqiang}, journal = {Frontiers in Neuroscience}, volume = {17}, pages = {1203104}, year = {2023}, publisher = {Frontiers Media SA}, doi = {10.3389/fnins.2023.1203104}, }
Brain Functional Network Generation Using Distribution-Regularized Adversarial Graph Autoencoder with Transformer for Dementia Diagnosis

Qiankun Zuo , Junhua Hu , Yudong Zhang^†, Junren Pan, Changhong Jing , Xuhang Chen, Xiaobo Meng, and Jin Hong^†

Computer Modeling in Engineering & Sciences: CMES, 2023

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The topological connectivity information derived from the brain functional network can bring new insights for diagnosing and analyzing dementia disorders. The brain functional network is suitable to bridge the correlation between abnormal connectivities and dementia disorders. However, it is challenging to access considerable amounts of brain functional network data, which hinders the widespread application of data-driven models in dementia diagnosis. In this study, a novel distribution-regularized adversarial graph auto-Encoder (DAGAE) with transformer is proposed to generate new fake brain functional networks to augment the brain functional network dataset, improving the dementia diagnosis accuracy of data-driven models. Specifically, the label distribution is estimated to regularize the latent space learned by the graph encoder, which can make the learning process stable and the learned representation robust. Also, the transformer generator is devised to map the node representations into node-to-node connections by exploring the long-term dependence of highly-correlated distant brain regions. The typical topological properties and discriminative features can be preserved entirely. Furthermore, the generated brain functional networks improve the prediction performance using different classifiers, which can be applied to analyze other cognitive diseases. Attempts on the Alzheimer’s Disease Neuroimaging Initiative (ADNI) dataset demonstrate that the proposed model can generate good brain functional networks. The classification results show adding generated data can achieve the best accuracy value of 85.33%, sensitivity value of 84.00%, specificity value of 86.67%. The proposed model also achieves superior performance compared with other related augmented models. Overall, the proposed model effectively improves cognitive disease diagnosis by generating diverse brain functional networks.
@article{Zuo:2023, title = {Brain Functional Network Generation Using Distribution-Regularized Adversarial Graph Autoencoder with Transformer for Dementia Diagnosis}, author = {Zuo, Qiankun and Hu, Junhua and Zhang, Yudong and Pan, Junren and Jing, Changhong and Chen, Xuhang and Meng, Xiaobo and Hong, Jin}, journal = {Computer Modeling in Engineering \& Sciences: CMES}, volume = {137}, number = {3}, pages = {2129-2147}, year = {2023}, publisher = {Europe PMC Funders}, doi = {10.32604/cmes.2023.028732}, }