Attention-guided Low-light Image Enhancement

Low-light image enhancement is a challenging task as multiple factors, including color, brightness, contrast, artifacts, and noise, etc. need to be simultaneously and effectively handled. To address such a complex problem containing multiple issues, this paper proposes a novel attention-guided enhancement solution based on which an end-to-end multi-branch CNN is built. The key of our method is the computation of two attention maps to guide the exposure enhancement and denoising tasks respectively. In particular, the first attention map distinguishes underexposed regions from well lit regions, while the second attention map distinguishes noises from real textures. Under their guidance, the proposed multi-branch enhancement network can work in an input adaptive way. Other contributions of this paper include a decomposition-and-fusion design of the enhancement network and the reinforcement-net for further contrast enhancement. In addition, we have proposed a large dataset for low-light enhancement. We evaluate the proposed method with extensive experiments, and the results demonstrate that our solution outperforms state-of-the-art methods by a large margin both quantitatively and visually. We additionally show that our method is flexible and effective for other image processing tasks.