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

Research output: Chapters, Conference Papers, Creative and Literary WorksRGC 32 - Refereed conference paper (with host publication)peer-review

282 Scopus Citations
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Author(s)

  • Wenhui Wu
  • Jian Weng
  • Xu Wang
  • Wenhan Yang
  • Jianmin Jiang

Related Research Unit(s)

Detail(s)

Original languageEnglish
Title of host publicationProceedings - 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition
Subtitle of host publicationCVPR 2022
PublisherInstitute of Electrical and Electronics Engineers, Inc.
Pages5891-5900
ISBN (electronic)9781665469463
ISBN (print)978-1-6654-6947-0
Publication statusPublished - 2022

Publication series

NameProceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition
ISSN (Print)1063-6919
ISSN (electronic)2575-7075

Conference

Title2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR 2022)
LocationHybrid
PlaceUnited States
CityNew Orleans
Period19 - 24 June 2022

Abstract

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.

Research Area(s)

  • Low-level vision

Citation Format(s)

URetinex-Net: Retinex-based Deep Unfolding Network for Low-light Image Enhancement. / Wu, Wenhui; Weng, Jian; Zhang, Pingping et al.
Proceedings - 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition: CVPR 2022. Institute of Electrical and Electronics Engineers, Inc., 2022. p. 5891-5900 (Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition).

Research output: Chapters, Conference Papers, Creative and Literary WorksRGC 32 - Refereed conference paper (with host publication)peer-review