Contrastive Learning for Low-light Raw Denoising
Taoyong Cui Yuhan Dong
Image/video denoising in low-light scenes is an extremely challenging problem
due to limited photon count and high noise. In this paper, we propose a novel
approach with contrastive learning to address this issue. Inspired by the
success of contrastive learning used in some high-level computer vision tasks,
we bring in this idea to the low-level denoising task. In order to achieve this
goal, we introduce a new denoising contrastive regularization (DCR) to exploit
the information of noisy images and clean images. In the feature space, DCR
makes the denoised image closer to the clean image and far away from the noisy
image. In addition, we build a new feature embedding network called Wnet, which
is more effective to extract high-frequency information. We conduct the
experiments on a real low-light dataset that captures still images taken on a
moonless clear night in 0.6 millilux and videos under starlight (no moon
present, <0.001 lux). The results show that our method can achieve a higher
PSNR and better visual quality compared with existing methods