KinD-LCE: Curve Estimation and Retinex Fusion on Low-Light Image

Abstract Low-light images often suffer from noise and color distortion. Object detection, semantic segmentation, instance segmentation, and other tasks are challenging when working with low-light images because of image noise and chromatic aberration. We also found that the conventional Retinex theory loses information in adjusting the image for low-light tasks. In response to the aforementioned problem, this paper proposes an algorithm for low illumination enhancement. The proposed method, KinD-LCE, uses a light curve estimation module to enhance the illumination map in the Retinex decomposed image, improving the overall image brightness. An illumination map and reflection map fusion module were also proposed to restore the image details and reduce detail loss. Additionally, a total variation loss function was applied to eliminate noise. The proposed method was trained using the GladNet dataset and tested with the Low-Light dataset. The ExDark dataset was used for validation in downstream tasks. The benchmark experiments demonstrated that the proposed algorithm achieved PSNR (19.7216) and SSIM (0.8213) values, which are close to state-of-the-art results.