Dual Degradation-Inspired Deep Unfolding Network for Low-Light Image Enhancement

• URL: http://arxiv.org/abs/2308.02776v2
• Date: Tue, 31 Dec 2024 04:16:56 GMT
• Title: Dual Degradation-Inspired Deep Unfolding Network for Low-Light Image Enhancement
• Authors: Huake Wang, Xingsong Hou, Chengcu Liu, Kaibing Zhang, Xiangyong Cao, Xueming Qian,
• Abstract summary: We propose a Dual degrAdation-inSpired deep Unfolding network, termed DASUNet, for low-light image enhancement. It learns two distinct image priors via considering degradation specificity between luminance and chrominance spaces. Based on different specificity in two spaces, we design two customized Transformer block to model different priors.
• Score: 22.959000860546578
• License:
• Abstract: 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. Based on different specificity in two spaces, we design two customized Transformer block to model different 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.

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