RetinexUNet++: A Retinex-Based UNet++ Model for Low-Light Image Enhancement

Low-light image enhancement (LLIE) aims to recover details in images captured under low-light conditions, crucial for improving the quality of low-level perception and enhancing the performance of high-level visual tasks. Most transformer-based methods show great potential in this field, but they rarely utilize the inherent structural details and color information of images that are unaffected by lighting, leading to susceptibility to noise interference when learning features. To address this issue, we propose a novel low-light image enhancement U-shaped network based on Retinex, called RetinexUNet++. Firstly, we design a reflectance perception module (RPM), which uses image intrinsic structure information as key clues to guide the network to learn low-light image features. Secondly, we design a transformer-based reinforced U-shaped network (RUN) to narrow the semantic gap between the encoder and decoder feature maps, learning multi-scale semantic features crucial for image restoration. Finally, to address the lack of real high-definition datasets in this field, we build a dataset of 6K resolution image pairs captured in desert low-light (D-LOL6K) scenes. Extensive experiments conducted on public datasets and the new dataset demonstrate that RetinexUNet++ outperforms the current state-of-the-art methods in both quantitative and visual performance.