Model ArchitectureAbstract
Image enhancement is a commonly employed technique in digital image processing, which aims to improve the aesthetic appeal and visual quality of an image. However, traditional enhancement approaches based on pixel-level or global-level modifications have limited effectiveness. With the increasing popularity of learning-based techniques, recent works have focused on utilizing various networks for image enhancement. Nevertheless, these methods often lack optimization of image frequency domains. To address this gap, this study introduces a transformer-based model for enhancing images in the wavelet domain. The proposed model refines different frequency bands of an image and prioritizes both local details and high-level features. As a result, the proposed method generates superior enhancement results. The performance evaluation of the model was assessed through comprehensive benchmark evaluations, which indicate that our method outperforms the state-of-the-art techniques.
