Purifying Low-Light Images via Near-Infrared Enlightened Image
Renjie Wan, Boxin Shi, Wenhan Yang, Bihan Wen, Ling‐Yu Duan, Alex C. Kot
Abstract
Cameras usually produce low-quality images under low-light conditions. Though many methods have been proposed to enhance the visibility of low-light images, they are mainly designed for illumination correction and less capable of sup-pressing the artifacts. In this paper, we propose to enhance the visibility and suppress artifacts by purifying low-light images under the guidance of the NIR enlightened image captured by using the near-infrared light as compensation. Specifically, we introduce a disentanglement framework to disentangle the structure and color components from the NIR enlightened and RGB images, respectively. Correspondingly, we introduce a new dataset with the RGB and NIR enlightened images for training and evaluation purposes. The experimental results show that our proposed method achieves promising results.