URetinex-Net: Retinex-based Deep Unfolding Network for Low-light Image Enhancement

By Wen-Bin Wu, Jian Weng, Pingping Zhang, Xu Wang, Wenhan Yang, Jianmin Jiang. Computer Vision and Pattern Recognition, 2022

713 citations recorded by the source corpus.

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.

Subfields: Computer Vision and Pattern Recognition

View Paper: https://www.semanticscholar.org/paper/87509a64ad473cfa78bd83c8fd06d86207b0951c

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In Computer Vision and Pattern Recognition

  1. Yue Huang
  2. Kai-Kuang Ma
  3. Yan Yang
  4. Xinghao Ding
  5. Xiaotong Tu
  6. Zhenqi Fu
  7. Wenhan Yang
  8. Jiaying Liu

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