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.

Keywords

Computer sciencePixelMonotonic functionRange (aeronautics)Differentiable functionArtificial intelligenceImage (mathematics)Curve fittingAlgorithmDeep learningFace (sociological concept)Nonlinear systemSet (abstract data type)Zero (linguistics)MathematicsComputer visionMachine learningMathematical analysisPhysics

Affiliated Institutions

Related Publications

Deep Colorization

This paper investigates into the colorization problem which converts a grayscale image to a colorful version. This is a very difficult problem and normally requires manual adjus...

2015 540 citations

Publication Info

Year
2020
Type
article
Citations
1861
Access
Closed

Social Impact

Social media, news, blog, policy document mentions

Citation Metrics

1861
OpenAlex
399
Influential
1698
CrossRef

Cite This

Chunle Guo, Chongyi Li, Jichang Guo et al. (2020). Zero-Reference Deep Curve Estimation for Low-Light Image Enhancement. 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) . https://doi.org/10.1109/cvpr42600.2020.00185

Identifiers

DOI
10.1109/cvpr42600.2020.00185
arXiv
2001.06826

Data Quality

Data completeness: 84%