Attention-Based Multi-Branch Network for Low-Light Image Enhancement

Abstract

Low-light conditions make the obtained images suffer a series of degradation, such as low contrast, noise interference and color distortion. Many previous learning-based methods have made remarkable progress, but they may still produce unsatisfactory results for ignoring noise in low-light regions. An attention-based multi-branch network is proposed, which can adequately enhance the image and suppress latent noise. The proposed method firstly estimates illumination component and reflectance component through a decomposition process. Then the illumination component is brightened to reconstruct the global lighting distribution, and the reflectance component is restored to remove noise and maintain details. A lightweight but effective attention block is employed to guide the restoration of the reflectance component, so as to concentrate on the distribution of lighting in different regions and effectively suppress noise in the dim environment. Extensive experiments on several datasets show the proposed method can achieve good results compared with classic and state-of-the-art methods.