The DOI matches its Crossref record; 53 of the 54 references it cites were found in a registry or an index.
LLDiffusion: Learning degradation representations in diffusion models for low-light image enhancement
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“Both PDB and MPB adopt convolution-based architectures to preserve local details and high-frequency components of the moir´ e pattern, following the findings in [47].”
From MoCHA-former: Moiré-Conditioned Hybrid Adaptive Transformer for Video Demoiréing (2026) · cites this paper as [47]
Citations
Cites
Proceedings of IEEE Conference on Computer Vision and Pattern Recognition
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The Unreasonable Effectiveness of Deep Features as a Perceptual Metric
Zhang, R., Isola, P., Efros, A. A., Shechtman, E., & Wang, O. (2018) · 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2018.00068 · Cited by 14,623
Proceedings of IEEE International Conference on Computer Vision
(1995) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.1995.466933 · Cited by 13,117
Proceedings European Conference on Computer Vision 2002
Heyden, A., Sparr, G., Nielsen, M., & Johansen, P. (2002) · Lund University Publications (Lund University) · Cited by 5,153
Restormer: Efficient Transformer for High-Resolution Image Restoration
Zamir, S. W., Arora, A., Khan, S., Hayat, M., Khan, F. S., & Yang, M. (2022) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr52688.2022.00564 · Cited by 4,737
LIME: Low-Light Image Enhancement via Illumination Map Estimation
Guo, X., Li, Y., & Ling, H. (2017) · IEEE Transactions on Image Processing · doi:10.1109/tip.2016.2639450 · Cited by 2,998
The Retinex Theory of Color Vision
Land, E. H. (1977) · Scientific American · doi:10.1038/scientificamerican1277-108 · Cited by 2,981
EnlightenGAN: Deep Light Enhancement Without Paired Supervision
Jiang, Y., Gong, X., Liu, D., Cheng, Y., Chen, F., Shen, X., Yang, J., Zhou, P., & Wang, Z. (2021) · IEEE Transactions on Image Processing · doi:10.1109/tip.2021.3051462 · Cited by 2,852
Zero-Reference Deep Curve Estimation for Low-Light Image Enhancement
Guo, C., Li, C., Guo, J., Loy, C. C., Hou, J., Kwong, S., & Cong, R. (2020) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr42600.2020.00185 · Cited by 2,632
Uformer: A General U-Shaped Transformer for Image Restoration
Wang, Z., Cun, X., Bao, J., Zhou, W., Liu, J., & Li, H. (2022) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr52688.2022.01716 · Cited by 2,597
Cited by
Sorted by citations
Diffusion Models for Image Restoration and Enhancement: A Comprehensive Survey
Li, X., Ren, Y., Jin, X., Lan, C., Wang, X., Zeng, W., Wang, X., & Chen, Z. (2025) · International Journal of Computer Vision · doi:10.1007/s11263-025-02570-9 · Cited by 219
On the design fundamentals of diffusion models: A survey
Chang, Z., Koulieris, G. A., Chang, H. J., & Shum, H. P. H. (2025) · Pattern Recognition · doi:10.1016/j.patcog.2025.111934 · Cited by 158
Diffusion Models in Low-Level Vision: A Survey
He, C., Shen, Y., Fang, C., Xiao, F., Tang, L., Zhang, Y., Zuo, W., Guo, Z., & Li, X. (2025) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2025.3545047 · Cited by 145
Self-citationLow-Light Image Enhancement via CLIP-Fourier Guided Wavelet Diffusion
Xue, M., He, J., Wang, W., & Zhou, M. (2025) · ACM Transactions on Multimedia Computing Communications and Applications · doi:10.1145/3764933 · Cited by 63
Kim, J., & Kim, T.-K. (2024) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr52733.2024.00879 · Cited by 38
Self-citation
Metadata
- Authors
- Wang, T., Zhang, K., Zhang, Y., Luo, W., Stenger, B., Lu, T., Kim, T.-K., & Liu, W.
- Affiliations
- Nanjing University (CN)
- Tencent (China) (CN)
- Hong Kong University of Science and Technology (HK)
- Rakuten (Japan) (JP)
- Korea Advanced Institute of Science and Technology (KR)
- Corresponding author
- Luo, Wenhan
- Venue
- Pattern Recognition
- Publisher
- Elsevier BV
- Published
- 2025-10
- Volume
- 166
- Pages
- 111628
- ISSN
- 0031-3203 (ISSN-L)
- OpenAlex
- W4409349224
- Cited by
- 150
- References
- 54
- Citations by year
- 2026: 87 · 2025: 56
- FWCI
- 50.38
- Type
- journal article
- Language
- en
- Open access
- green
- arxiv.org
- Licence
- Locations
- Pattern Recognition (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- Rare & Special e-Zone (The Hong Kong University of Science and Technology) (submittedVersion)
- arXiv (Cornell University)
- Funders
- National Natural Science Foundation of China (U24A20330, 62372480, 62372223)
- China Computer Federation
- Natural Science Foundation of Shenzhen Municipality (JCYJ20240813110459017)
- Basic and Applied Basic Research Foundation of Guangdong Province (2025A1515011361)
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Image Enhancement Techniques
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Image Processing Techniques
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Image and Signal Denoising Methods
- Keywords
- low-light image enhancement, diffusion models, noise amplification, color distortion, contrast reduction, image priors
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
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