The DOI matches its Crossref record; 46 of the 46 references it cites were found in a registry or an index.
Learning to Enhance Low-Light Image via Zero-Reference Deep Curve Estimation
Open accessAbstract
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.
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Cites
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Unpaired image-to-image translation using cycle-consistent adversarial networks
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Xception: Deep Learning with Depthwise Separable Convolutions
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LIME: Low-Light Image Enhancement via Illumination Map Estimation
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The Retinex Theory of Color Vision
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EnlightenGAN: Deep Light Enhancement Without Paired Supervision
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Zero-Reference Deep Curve Estimation for Low-Light Image Enhancement
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Self-citationAn Underwater Image Enhancement Benchmark Dataset and Beyond
Li, C., Guo, C., Ren, W., Cong, R., Hou, J., Kwong, S., & Tao, D. (2020) · IEEE Transactions on Image Processing · doi:10.1109/tip.2019.2955241 · Cited by 2,373
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Cited by
Underwater Image Enhancement via Minimal Color Loss and Locally Adaptive Contrast Enhancement
Zhang, W., Zhuang, P., Sun, H.-H., Li, G., Kwong, S., & Li, C. (2022) · IEEE Transactions on Image Processing · doi:10.1109/tip.2022.3177129 · Cited by 888
Self-citationLow-Light Image and Video Enhancement Using Deep Learning: A Survey
Li, C., Guo, C., Han, L., Jiang, J., Cheng, M.-M., Gu, J., & Loy, C. C. (2022) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2021.3126387 · Cited by 657
Self-citationUltra-High-Definition Low-Light Image Enhancement: A Benchmark and Transformer-Based Method
Wang, T., Zhang, K., Shen, T., Luo, W., Stenger, B., & Lu, T. (2023) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v37i3.25364 · Cited by 549
Underwater Image Enhancement via Weighted Wavelet Visual Perception Fusion
Zhang, W., Zhou, L., Zhuang, P., Li, G., Pan, X., Zhao, W., & Li, C. (2024) · IEEE Transactions on Circuits and Systems for Video Technology · doi:10.1109/tcsvt.2023.3299314 · Cited by 406
Self-citationXiao, J., Fu, X., Liu, A., Wu, F., & Zha, Z.-J. (2023) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2022.3183612 · Cited by 389
Metadata
- Authors
- Li, C., Guo, C., & Chen, C. L.
- Venue
- IEEE Transactions on Pattern Analysis and Machine Intelligence
- Published
- 2021
- Volume
- 44
- Issue
- 8
- Pages
- 1-1
- ISSN
- 0162-8828 (ISSN-L)
- 2160-9292
- 1939-3539
- OpenAlex
- W3134649899
- Cited by
- 719
- References
- 46
- FWCI
- 36.60
- Type
- journal article
- Language
- en
- Open access
- green
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