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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]
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Cites
Proceedings of IEEE Conference on Computer Vision and Pattern Recognition
(1993) · doi:10.1109/cvpr.1993 · Cited by 17,989
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
Pre-Trained Image Processing Transformer
Chen, H., Wang, Y., Guo, T., Xu, C., Deng, Y., Liu, Z., Ma, S., Xu, C., Xu, C., & Gao, W. (2021) · 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr46437.2021.01212 · Cited by 2,308
LLNet: A deep autoencoder approach to natural low-light image enhancement
Lore, K. G., Akintayo, A., & Sarkar, S. (2016) · Pattern Recognition · doi:10.1016/j.patcog.2016.06.008 · Cited by 1,973
Proceedings IEEE International Conference on Acoustics Speech and Signal Processing
Zheng, F., McLaughlin, S., & Mulgrew, B. (1991) · Cited by 1,906
Image Super-Resolution Via Iterative Refinement
Saharia, C., Ho, J., Chan, W., Salimans, T., Fleet, D. J., & Norouzi, M. (2022) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2022.3204461 · Cited by 1,829
Naturalness Preserved Enhancement Algorithm for Non-Uniform Illumination Images
Wang, S., Zheng, J. Y., Hu, H., & Li, B. H. (2013) · IEEE Transactions on Image Processing · doi:10.1109/tip.2013.2261309 · Cited by 1,772
Deep Retinex Decomposition for Low-Light EnhancementPreprint
Wei, C., Wang, W., Yang, W., & Liu, J. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1808.04560 · Cited by 1,550
Proceedings of the British Machine Vision Conference 2014
(2014) · doi:10.5244/c.28 · Cited by 1,360
A Feature-Enriched Completely Blind Image Quality Evaluator
Zhang, L., Zhang, L., & Bovik, A. C. (2015) · IEEE Transactions on Image Processing · doi:10.1109/tip.2015.2426416 · Cited by 1,311
Learning a Deep Single Image Contrast Enhancer from Multi-Exposure Images
Cai, J., Gu, S., & Lei Zhang, A. (2018) · IEEE Transactions on Image Processing · doi:10.1109/tip.2018.2794218 · Cited by 1,246
Toward Convolutional Blind Denoising of Real Photographs
Guo, S., Yan, Z., Zhang, K., Zuo, W., & Zhang, L. (2019) · 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2019.00181 · Cited by 1,221
Self-citationAdaptive image contrast enhancement using generalizations of histogram equalization
Stark, J. A. (2000) · IEEE Transactions on Image Processing · doi:10.1109/83.841534 · Cited by 1,202
Perceptual Quality Assessment for Multi-Exposure Image Fusion
Ma, K., Zeng, K., & Wang, Z. (2015) · IEEE Transactions on Image Processing · doi:10.1109/tip.2015.2442920 · Cited by 1,197
Liu, R., Ma, L., Zhang, J., Fan, X., & Luo, Z. (2021) · 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/cvpr46437.2021.01042 · Cited by 1,133
Learning Enriched Features for Real Image Restoration and Enhancement
Zamir, S. W., Arora, A., Khan, S., Hayat, M., Khan, F. S., Yang, M., & Shao, L. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-58595-2_30 · Cited by 1,107
Underexposed Photo Enhancement Using Deep Illumination Estimation
Wang, R., Zhang, Q., Fu, C., Shen, X., Zheng, W., & Jia, J. (2019) · 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/cvpr.2019.00701 · Cited by 1,044
Beyond Brightening Low-light Images
Zhang, Y., Guo, X., Ma, J., Liu, W., & Zhang, J. (2021) · International Journal of Computer Vision · doi:10.1007/s11263-020-01407-x · Cited by 1,021
Self-citationSparse Gradient Regularized Deep Retinex Network for Robust Low-Light Image Enhancement
Yang, W., Wang, W., Huang, H., Wang, S., & Liu, J. (2021) · IEEE Transactions on Image Processing · doi:10.1109/tip.2021.3050850 · Cited by 846
A fusion-based enhancing method for weakly illuminated images
Fu, X., Zeng, D., Huang, Y., Liao, Y., Ding, X., & Paisley, J. W. (2016) · Signal Processing · doi:10.1016/j.sigpro.2016.05.031 · Cited by 742
Deep Unfolding Network for Image Super-Resolution
Zhang, K., Gool, L. V., & Timofte, 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/cvpr42600.2020.00328 · Cited by 689
Self-citationFrom Fidelity to Perceptual Quality: A Semi-Supervised Approach for Low-Light Image Enhancement
Yang, W., Wang, S., Fang, Y., Wang, Y., & Liu, J. (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/cvpr42600.2020.00313 · Cited by 683
Ultra-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 582
Self-citationRestoring Vision in Adverse Weather Conditions With Patch-Based Denoising Diffusion Models
Özdenizci, O., & Legenstein, R. A. (2023) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2023.3238179 · Cited by 525
Deblurring by Realistic Blurring
Zhang, K., Luo, W., Zhong, Y., Ma, L., Stenger, B., Liu, W., & Li, H. (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/cvpr42600.2020.00281 · Cited by 489
Self-citationLearning to Restore Low-Light Images via Decomposition-and-Enhancement
Xu, K., Yang, X. S., Yin, B., & Lau, R. W. H. (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/cvpr42600.2020.00235 · Cited by 484
Proceedings of the 26th ACM international conference on Multimedia
(2018) · doi:10.1145/3240508 · Cited by 459
Benchmarking Low-Light Image Enhancement and Beyond
Liu, J., Xu, D., Yang, W., Fan, M., & Huang, H. (2021) · International Journal of Computer Vision · doi:10.1007/s11263-020-01418-8 · Cited by 379
Proceedings International Conference On Image Processing
(1997) · Proceedings - International Conference on Image Processing · doi:10.1109/icip.1997.638657 · Cited by 341
Chen, C., Chen, Q., Do, M. N., & Koltun, V. (2019) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2019.00328 · Cited by 310
DeepLPF: Deep Local Parametric Filters for Image Enhancement
Moran, S., Marza, P., McDonagh, S., Parisot, S., & Slabaugh, G. (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/cvpr42600.2020.01284 · Cited by 296
Contrast enhancement based on layered difference representation
Lee, C., Lee, C., & Kim, C. (2012) · Proceedings - International Conference on Image Processing · doi:10.1109/icip.2012.6467022 · Cited by 279
A Bio-Inspired Multi-Exposure Fusion Framework for Low-light Image EnhancementPreprint
Ying, Z., Li, G., & Gao, W. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1711.00591 · Cited by 247
Kindling the Darkness: A Practical Low-light Image EnhancerPreprint
Zhang, Y., Zhang, J., & Guo, X. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1905.04161 · Cited by 217
Self-citationShadowDiffusion: When Degradation Prior Meets Diffusion Model for Shadow Removal
Guo, L., Wang, C., Yang, W., Huang, S., Wang, Y., Pfister, H., & Wen, B. (2023) · 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/cvpr52729.2023.01350 · Cited by 167
Toward High-Quality HDR Deghosting With Conditional Diffusion Models
Yan, Q., Hu, T., Sun, Y., Tang, H., Zhu, Y., Dong, W. F., Gool, L. V., & Zhang, Y. (2023) · IEEE Transactions on Circuits and Systems for Video Technology · doi:10.1109/tcsvt.2023.3326293 · Cited by 140
Self-citationUnpaired Image Enhancement Featuring Reinforcement-Learning-Controlled Image Editing Software
Kosugi, S., & Yamasaki, T. (2020) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v34i07.6790 · Cited by 107
Low-light image enhancement using gamma correction prior in mixed color spaces
Jeon, J. J., Park, J. Y., & Eom, I. K. (2023) · Pattern Recognition · doi:10.1016/j.patcog.2023.110001 · Cited by 93
Restoring vision in hazy weather with hierarchical contrastive learning
Wang, T., Tao, G., Lu, W., Zhang, K., Luo, W., Zhang, X., & Lü, T. (2023) · Pattern Recognition · doi:10.1016/j.patcog.2023.109956 · Cited by 89
Self-citationMulti-metric learning for multi-sensor fusion based classification
Zhang, Y., Zhang, H., Nasrabadi, N. M., & Huang, T. S. (2012) · Information Fusion · doi:10.1016/j.inffus.2012.05.002 · Cited by 84
Self-citationDenoising diffusion post-processing for low-light image enhancement
Panagiotou, S., & Bosman, A. S. (2024) · Pattern Recognition · doi:10.1016/j.patcog.2024.110799 · Cited by 66
Data Augmentation of Contrastive Learning is Estimating Positive-incentive NoisePreprint
Zhang, H., Xu, Y., Huang, S., & Li, X. (2024) · arXiv (Cornell University) · doi:10.48550/arxiv.2408.09929 · Cited by 66
Enhance Vision-Language Alignment with Noise
Huang, S., Zhang, H., & Li, X. (2025) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v39i16.33918 · Cited by 49
A reflectance re-weighted Retinex model for non-uniform and low-light image enhancement
Jia, F., Wong, H. S., Wang, T., & Zeng, T. (2023) · Pattern Recognition · doi:10.1016/j.patcog.2023.109823 · Cited by 45
Self-citationEiffHDR: An Efficient Network for Multi-Exposure High Dynamic Range Imaging
Zhang, X., Zhu, Q., Hu, T., & Yan, Q. (2024) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp48485.2024.10446711 · Cited by 6
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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
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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
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