The DOI matches its Crossref record; 31 of the 31 references it cites were found in a registry or an index.
Ultra-High-Definition Low-Light Image Enhancement: A Benchmark and Transformer-Based Method
Open accessAbstract
As the quality of optical sensors improves, there is a need for processing large-scale images. In particular, the ability of devices to capture ultra-high definition (UHD) images and video places new demands on the image processing pipeline. In this paper, we consider the task of low-light image enhancement (LLIE) and introduce a large-scale database consisting of images at 4K and 8K resolution. We conduct systematic benchmarking studies and provide a comparison of current LLIE algorithms. As a second contribution, we introduce LLFormer, a transformer-based low-light enhancement method. The core components of LLFormer are the axis-based multi-head self-attention and cross-layer attention fusion block, which significantly reduces the linear complexity. Extensive experiments on the new dataset and existing public datasets show that LLFormer outperforms state-of-the-art methods. We also show that employing existing LLIE methods trained on our benchmark as a pre-processing step significantly improves the performance of downstream tasks, e.g., face detection in low-light conditions. The source code and pre-trained models are available at https://github.com/TaoWangzj/LLFormer.
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Exploiting Generative AI to Scale up Intelligent Tutoring Systems
Jakubův, J., Chvalovský, K., Goertzel, Z., Kaliszyk, C., Olšák, M., Piotrowski, B., Schulz, S., Suda, M., & Urban, J. (2023) · DROPS (Schloss Dagstuhl – Leibniz Center for Informatics) · doi:10.4230/lipics.itp.2023.19 · Cited by 76,658
An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale
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Swin Transformer: Hierarchical Vision Transformer using Shifted Windows
Liu, Z., Lin, Y., Cao, Y., Hu, H., Wei, Y., Zhang, Z., Lin, S., & Guo, B. (2021) · 2021 IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv48922.2021.00986 · Cited by 35,534
Shi, W., Caballero, J., Huszar, F., Totz, J., Aitken, A. P., Bishop, R., Rueckert, D., & Wang, Z. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.207 · Cited by 7,570
Restormer: Efficient Transformer for High-Resolution Image Restoration
Zamir, S. W., Arora, A., Khan, S., Hayat, M., Khan, F. S., & Yang, M.-H. (2022) · 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr52688.2022.00564 · Cited by 4,143
A multiscale retinex for bridging the gap between color images and the human observation of scenes
Jobson, D. J., Rahman, Z., & Woodell, G. A. (1997) · IEEE Transactions on Image Processing · doi:10.1109/83.597272 · Cited by 3,036
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
EnlightenGAN: Deep Light Enhancement Without Paired Supervision
Jiang, Y., Gong, X., Liu, D., Cheng, Y., Fang, C., Shen, X., Yang, J., Zhou, P., & Wang, Z. (2021) · IEEE Transactions on Image Processing · doi:10.1109/tip.2021.3051462 · Cited by 2,715
Zero-Reference Deep Curve Estimation for Low-Light Image Enhancement
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Uformer: A General U-Shaped Transformer for Image Restoration
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Cited by
Diff-Retinex: Rethinking Low-light Image Enhancement with A Generative Diffusion Model
Yi, X., Xu, H., Zhang, H., Tang, L., & Ma, J. (2023) · 2023 IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv51070.2023.01130 · Cited by 305
Wang, T., Zhang, K., Zhang, Y., Luo, W., Stenger, B., Lu, T., Kim, T.-K., & Liu, W. (2025) · Pattern Recognition · doi:10.1016/j.patcog.2025.111628 · Cited by 150
Self-citationMoCHA-former: Moiré-Conditioned Hybrid Adaptive Transformer for Video Demoiréing
Sung, J., Roh, C., Eom, C., & Oh, J. (2026) · Neurocomputing · doi:10.1016/j.neucom.2025.132477
Metadata
- Authors
- Wang, T., Zhang, K., Shen, T., Luo, W., Stenger, B., & Lu, T.
- Venue
- Proceedings of the AAAI Conference on Artificial Intelligence
- Published
- 2023-06-26
- Volume
- 37
- Issue
- 3
- Pages
- 2654-2662
- ISSN
- 2159-5399 (ISSN-L)
- 2374-3468
- OpenAlex
- W4382240242
- Cited by
- 549
- References
- 31
- FWCI
- 62.42
- Type
- conference paper
- Language
- en
- Open access
- diamond
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- Wang, T., Zhang, K., Shen, T., Luo, W., Stenger, B., & Lu, T. (2023). Ultra-High-Definition Low-Light Image Enhancement: A Benchmark and Transformer-Based Method. Proceedings of the AAAI Conference on Artificial Intelligence. https://doi.org/10.1609/aaai.v37i3.25364
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