Dual Degradation-Inspired Deep Unfolding Network for Low-Light Image
  Enhancement
Huake Wang Xingsong Hou Xiaoyang Yan
Although low-light image enhancement has achieved great stride based on deep
enhancement models, most of them mainly stress on enhancement performance via
an elaborated black-box network and rarely explore the physical significance of
enhancement models. Towards this issue, we propose a Dual degrAdation-inSpired
deep Unfolding network, termed DASUNet, for low-light image enhancement.
Specifically, we construct a dual degradation model (DDM) to explicitly
simulate the deterioration mechanism of low-light images. It learns two
distinct image priors via considering degradation specificity between luminance
and chrominance spaces. To make the proposed scheme tractable, we design an
alternating optimization solution to solve the proposed DDM. Further, the
designed solution is unfolded into a specified deep network, imitating the
iteration updating rules, to form DASUNet. Local and long-range information are
obtained by prior modeling module (PMM), inheriting the advantages of
convolution and Transformer, to enhance the representation capability of dual
degradation priors. Additionally, a space aggregation module (SAM) is presented
to boost the interaction of two degradation models. Extensive experiments on
multiple popular low-light image datasets validate the effectiveness of DASUNet
compared to canonical state-of-the-art low-light image enhancement methods. Our
source code and pretrained model will be publicly available.