Key points are not available for this paper at this time.
Retinex-based deep learning methods show good low-light enhancement performance and are mainstream approaches in this field. However, the current methods for enhancing low-light images are insufficient in accurately separating illumination and comprehensively restoring degraded information, especially in images with uneven or extremely low illumination levels. This situation often leads to the over-enhancement of bright regions, a loss of detail, and color distortion in the final images. To address these issues, we improved three subnetworks in the classic KinD network, and proposed a trans-scale and refined low-light image enhancement network. Compared with KinD, our method shows more precise image decomposition performance, enhancing the expressiveness of the reflection and illumination components in order to better depict image details, colors, and lighting information. For reflectance restoration, we use a U-shaped network for cross-scale denoising, incorporating attention mechanisms and a color saturation loss to restore image textures and colors. For light adjustment, we apply fine-grained light adjustment approaches to simultaneously enhance brightness in dark areas and prevent excessive enhancement in bright areas. The experimental results demonstrate that with the LOL dataset, the peak signal-to-noise ratio (PSNR) and structural similarity index measure (SSIM) of TSRNet are improved by 2–31% and 5–34%, respectively, when compared with the mainstream methods.
Mu et al. (2024) studied this question.