Title: Color-wise Attention Network for Low-light Image Enhancement Show Chinese title

Title: 基于颜色的注意力网络用于低光图像增强

Abstract: 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. Show Chinese abstract

Abstract: 在捕获图像时缺乏附近的光源会降低图像的可见性和质量，从而使计算机视觉任务变得困难。 在本文中，提出了一种基于卷积神经网络的色彩注意力网络（CWAN），用于低光图像增强。 受人类在查看暗图像时视觉系统启发，CWAN在搜索低光图像中任何有用的色彩线索以帮助色彩增强过程的同时，学习低光图像和增强图像之间的端到端映射。 一旦识别出这些区域，CWAN注意力将主要集中在合成这些局部区域以及全局图像上。 在具有挑战性的数据集上的定量和定性实验表明，与最先进方法相比，我们的方法具有优势。