Joint Self-Supervised Enhancement and Denoising of Low-Light Images

Images taken under low-light conditions often suffer from multiple degradations such as low visibility and unknown noise. Low-light image enhancement is an important task in the field of computer vision. In order to avoid the limited number of samples in paired datasets, several self-supervised enhancement methods have been developed. However, due to the designed illumination gradient prior, most self-supervised enhancement methods based on Retinex cannot effectively constrain the illumination or suppress the amplified real noise. To solve this problem, this paper explores a joint self-supervised enhancement and denoising method for low-light image. Initially, we proposed a new regularization term, named TV-Huber, and developed an adaptive illumination estimation network (AIE-Net) to explore the intrinsic relationship between structure and texture in the illumination map. Next, the camera response model and the learned illumination are then used to enhance the contrast of low-light images and mitigate color shifts. Finally, the learned illumination maps are transformed into illumination masks. Under the assumption of independent and zero-mean noise, selective feature injection is performed on the shallow features extracted by the blind-spot network (BSN) to reduce information loss while removing unknown real noise in the dark area. Extensive experiments show that the proposed method has good generalization ability on five challenging low-light image datasets and outperforms other methods in terms of visual quality and quantitative comparison.