MARN: Multi-Scale Attention Retinex Network for Low-Light Image Enhancement

Images captured in low-light conditions often suffer from bad visibility, e.g., low contrast, lost details, and color distortion, and image enhancement methods can be used to improve the image quality. Previous methods have generally obtained a smooth illumination map to enhance the image but have ignored details, leading to inaccurate illumination estimations. To solve this problem, we propose a multi-scale attention retinex network (MARN) for low-light image enhancement, which learns an image-to-illumination mapping to obtain a detailed inverse illumination map inspired by retinex theory. In order to introduce more image priors, we introduce a novel illuminance-attention map to guide the model to characterize varying-lighting areas, which we combine with the low-light image as the model input. MARN consists of a multi-scale attention module and a feature fusion module; the former extracts multi-resolution features with attention-based feature aggregation, while the latter further merges the output features of the previous module with the input. To achieve better visibility, we formulate a novel loss function to synthetically measure the illumination, detail, and colorfulness of the image. Extensive experiments are performed on several benchmark datasets. The results demonstrate that our method outperforms other state-of-the-art methods according to both objective and subjective metrics.