LDRM: Degradation Rectify Model for Low-light Imaging via Color-Monochrome Cameras

Junhong Lin, Shufan Pei, Bing Chen, Nanfeng Jiang, Wei Gao, Tiesong Zhao

Abstract: Low-light imaging task aims to approximate low-light scenes as perceived by human eyes. Existing methods usually pursue higher brightness, resulting in unrealistic exposure. Inspired by Human Vision System (HVS), where rods perceive more lights while cones perceive more colors, we propose a Low-light Degradation Rectify Model (LDRM) with color-monochrome cameras to solve this problem. First, we propose to use a low-ISO color camera and a high-ISO monochrome camera for low-light imaging under short-exposure of less than 0.1s. Short-exposure could avoid motion blurriness, while monochrome camera captures more photons than color camera. By mimicing HVS, this capture system could benefit low-light imaging. Second, we propose an LDRM model to fuse the color-monochrome image pair into a high-quality image. In this model, we separately restore UV and Y channels through chrominance and luminance branches and use monochrome image to guide the restoration of luminance. We also propose a latent code embedding method to improve the restorations of both branches. Third, we create a Low-light Color-Monochrome benchmark (LCM), including both synthetic and real-world datasets, to examine low-light imaging quality of LDRM and the state-of-the-art methods. Experimental results demonstrate the superior performance of LDRM with visually pleasing results. Codes and datasets are available at https://github.com/StephenLinn/LDRM.