Zero-Reference Deep Curve Estimation for Low-Light Image Enhancement

Research output: Chapters, Conference Papers, Creative and Literary Works (RGC: 12, 32, 41, 45)32_Refereed conference paper (with ISBN/ISSN)

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Author(s)

  • Chunle Guo
  • Chongyi Li
  • Jichang Guo
  • Chen Change Loy
  • Runmin Cong

Related Research Unit(s)

Detail(s)

Original languageEnglish
Title of host publicationProceedings 2020 IEEE/CVF International Conference on Computer Vision and Pattern Recognition
PublisherIEEE
Pages1777-1786
ISBN (Electronic)9781728171685
ISBN (Print)9781728171692
Publication statusPublished - Jun 2020

Publication series

Name
ISSN (Print)1063-6919
ISSN (Electronic)2575-7075

Conference

Title2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR 2020)
LocationVirtual
PlaceUnited States
CitySeattle
Period13 - 19 June 2020

Abstract

The paper presents a novel method, Zero-Reference Deep Curve Estimation (Zero-DCE), which formulates light enhancement as a task of image-specific curve estimation with a deep network. Our method trains a lightweight deep network, DCE-Net, to estimate pixel-wise and high-order curves for dynamic range adjustment of a given image. The curve estimation is specially designed, considering pixel value range, monotonicity, and differentiability. Zero-DCE is appealing in its relaxed assumption on reference images, i.e., it does not require any paired or unpaired data during training. This is achieved through a set of carefully formulated non-reference loss functions, which implicitly measure the enhancement quality and drive the learning of the network. Our method is efficient as image enhancement can be achieved by an intuitive and simple nonlinear curve mapping. Despite its simplicity, we show that it generalizes well to diverse lighting conditions. Extensive experiments on various benchmarks demonstrate the advantages of our method over state-of-the-art methods qualitatively and quantitatively. Furthermore, the potential benefits of our Zero-DCE to face detection in the dark are discussed.

Bibliographic Note

Research Unit(s) information for this publication is provided by the author(s) concerned.

Citation Format(s)

Zero-Reference Deep Curve Estimation for Low-Light Image Enhancement. / Guo, Chunle; Li, Chongyi; Guo, Jichang; Loy, Chen Change; Hou, Junhui; Kwong, Sam; Cong, Runmin.

Proceedings 2020 IEEE/CVF International Conference on Computer Vision and Pattern Recognition. IEEE, 2020. p. 1777-1786.

Research output: Chapters, Conference Papers, Creative and Literary Works (RGC: 12, 32, 41, 45)32_Refereed conference paper (with ISBN/ISSN)