  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.
