Contrastive Learning in Wavelet Domain for Image Dehazing

Abstract

Image dehazing remains a challenging problem because it is hard to restore a clean scene from a severely degraded hazy image. However, existing learning-based dehazing methods mostly ignore the fact that the interference of haze to an image is mainly concentrated in the low-frequency components. If all image components are processed indiscriminately, it is difficult to achieve a good restoration and accurate details cannot be guaranteed. In order to process the hazy images hierarchically, we propose a low-frequency sub-band contrastive regularization (LSCR) in the wavelet domain to ensure that the components of the restored image mainly affected by haze are pulled closer to the clear image and pushed far away from the hazy image. In addition, a high-frequency sub-band loss is also introduced to make high-frequency components of the restored image consistent with the clear image. Our method can better restore the haze-free image and achieve more accurate and rich details. The extensive experiments on synthetic and real-world datasets verify that the proposed method outperforms previous approaches.