Attention-guided Low-light Image Enhancement
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Low-light image enhancement is a challenging task since various factors, including brightness, contrast, artifacts and noise, should be handled simultaneously and effectively. To address such a difficult problem, this paper proposes a novel attention-guided enhancement solution and delivers the corresponding end-to-end multi-branch CNNs. The key of our method is the computation of two attention maps to guide the exposure enhancement and denoising respectively. In particular, the first attention map distinguishes underexposed regions from normally exposed regions, while the second attention map distinguishes noises from real-world textures. Under their guidance, the proposed multi-branch enhancement network can work in an adaptive way. Other contributions of this paper include the "decomposition/multi-branch-enhancement/fusion" design of the enhancement network, the reinforcement-net for contrast enhancement, and the proposed large-scale low-light enhancement dataset. We evaluate the proposed method through extensive experiments, and the results demonstrate that our solution outperforms state-of-the-art methods by a large margin. We additionally show that our method is flexible and effective for other image processing tasks.
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