Multi-camera systems have become extensively integrated into smartphones. One prominent implementation employs a color-monochrome dual-camera system, which synergistically combines data from both sensors to produce high-quality color images. The dual-camera system can capture asymmetric image pairs, consisting of relatively high-resolution monochrome images and low-resolution color images. Existing methods do not effectively address the issues of the inconsistency in spectral and spatial resolutions between color-monochrome image pairs in asymmetric color transfer. In this paper, we propose a novel framework based on YUV color space to address these issues. First, we conduct some analyses and experiments on several color spaces, demonstrating the effectiveness of asymmetric color transfer in YUV color space. Second, we design a YUV-color-space-based Asymmetric Color Transfer Network (YACT-Net) for reference-based colorization. In this network, we use a restoration module and a chrominance reconstruction module that utilize YUV information from image pairs to generate high-quality color images. Third, we create a Stereo Color Transfer (SCT) dataset, which includes both synthetic and real-world datasets, to evaluate the performance of YACT-Net. Experimental results demonstrate that YACT-Net achieves state-of-the-art performance in both quantitative and qualitative results. Our code and dataset are available athttps://github.com/fengshenghong/YACT-Net.

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.