初始化
先验概率
颜色恒定性
计算机科学
人工智能
图像(数学)
编码(集合论)
深度学习
网(多面体)
噪音(视频)
分解
计算机视觉
算法
数学
生态学
贝叶斯概率
几何学
集合(抽象数据类型)
生物
程序设计语言
作者
Wenhui Wu,Jian Weng,Pingping Zhang,Xu Wang,Wenhan Yang,Jianmin Jiang
标识
DOI:10.1109/cvpr52688.2022.00581
摘要
Retinex model-based methods have shown to be effective in layer-wise manipulation with well-designed priors for low-light image enhancement. However, the commonly used handcrafted priors and optimization-driven solutions lead to the absence of adaptivity and efficiency. To address these issues, in this paper, we propose a Retinex-based deep unfolding network (URetinex-Net), which unfolds an optimization problem into a learnable network to decompose a low-light image into reflectance and illumination layers. By formulating the decomposition problem as an implicit priors regularized model, three learning-based modules are carefully designed, responsible for data-dependent initialization, high-efficient unfolding optimization, and user-specified illumination enhancement, respectively. Particularly, the proposed unfolding optimization module, introducing two networks to adaptively fit implicit priors in data-driven manner, can realize noise suppression and details preservation for the final decomposition results. Extensive experiments on real-world low-light images qualitatively and quantitatively demonstrate the effectiveness and superiority of the proposed method over state-of-the-art methods. The code is available at https://github.com/AndersonYong/URetinex-Net.
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