Absence of nearby light sources while capturing an image will degrade the
visibility and quality of the captured image, making computer vision tasks
difficult. In this paper, a color-wise attention network (CWAN) is proposed for
low-light image enhancement based on convolutional neural networks. Motivated
by the human visual system when looking at dark images, CWAN learns an
end-to-end mapping between low-light and enhanced images while searching for
any useful color cues in the low-light image to aid in the color enhancement
process. Once these regions are identified, CWAN attention will be mainly
focused to synthesize these local regions, as well as the global image. Both
quantitative and qualitative experiments on challenging datasets demonstrate
the advantages of our method in comparison with state-of-the-art methods.

本文提出基于卷积神经网络的针对低光图像的色彩注意力网络（CWAN），学习了从低光到高质图像的端到端映射，并且在低光图像中寻找有用的色彩线索来辅助色彩增强过程，定位这些区域并将 CWAN 的注意力主要集中在综合这些局部区域以及整个全局图像上，基于挑战性数据集的定量和定性实验证明了该方法相对于现有技术的优势。