Learning to Enhance Low-Light Image via Zero-Reference Deep Curve Estimation

By Chongyi Li, Chunle Guo, Chen Change Loy. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2021

854 citations recorded by the source corpus.

This 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 even 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. Despite its simplicity, we show that it generalizes well to diverse lighting conditions. Our method is efficient as image enhancement can be achieved by an intuitive and simple nonlinear curve mapping. We further present an accelerated and light version of Zero-DCE, called Zero-DCE++, that takes advantage of a tiny network with just 10K parameters. Zero-DCE++ has a fast inference speed (1000/11 FPS on a single GPU/CPU for an image of size 1200×900×3) while keeping the enhancement performance of Zero-DCE. 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 method to face detection in the dark are discussed. The source code is made publicly available at https://li-chongyi.github.io/Proj_Zero-DCE++.html.

Subfields: Computer Vision and Pattern Recognition

View Paper: https://www.semanticscholar.org/paper/35fce9473e1ba34e97e91bcd85e30b6af6cf7ab6

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Computer Vision and Pattern Recognition

1. Chongyi Li
2. Chunle Guo
3. Runmin Cong
4. Peixian Zhuang
5. Sam Kwong
6. Qingming Huang
7. Wei Zhang
8. Tong Lu

Citations in the corpus

• Image De-Raining Transformer (2022, neutral): “…tasks [17], [18], [31], [32], [33], [34], [35], such as de-noising [36], [37], [38], super-resolution [39], [40], [41], de-blurring [42], [43], [44], [45], de-hazing [19], [19], [46], [47], [48], [49], [50], low-light image enhancement [51], [52], [53], [54], [55], style transfer [56], [57], etc .”
• PUGAN: Physical Model-Guided Underwater Image Enhancement Using GAN With Dual-Discriminators (2023, background): “light scene [5], [6], [7] or foggy scene [8], [9], [10], [11]) do not generalize well to the UIE task.”
• Ultra-High-Definition Low-Light Image Enhancement: A Benchmark and Transformer-Based Method (2022, methodology): “For benchmarking, we compare 16 representative LLIE methods, including seven traditional non-learning methods (BIMEF (Ying, Li, and Gao 2017), FEA (Dong et al. 2011), LIME (Guo, Li, and Ling 2016), MF (Fu et al. 2016a), NPE (Wang et al. 2013), SRIE (Fu et al. 2016b), MSRCR (Jobson, Rahman, and Woode”
• Underwater Image Enhancement via Weighted Wavelet Visual Perception Fusion (2024, methodology): “Depending on the powerful computing devices and abundant training data, deep learning methods are gradually applied to low-level visual tasks [45], [46], [47], [48].”