The DOI matches its Crossref record; 60 of the 60 references it cites were found in a registry or an index.
SNR-Aware Low-light Image Enhancement
Abstract
This paper presents a new solution for low-light image enhancement by collectively exploiting Signal-to-Noise-Ratio-aware transformers and convolutional models to dynamically enhance pixels with spatial-varying operations. They are long-range operations for image regions of extremely low Signal-to-Noise-Ratio (SNR) and short-range operations for other regions. We propose to take an SNR prior to guide the feature fusion and formulate the SNR-aware transformer with a new self-attention model to avoid tokens from noisy image regions of very low SNR. Extensive experiments show that our framework consistently achieves better performance than SOTA approaches on seven representative benchmarks with the same structure. Also, we conducted a large-scale user study with 100 participants to verify the superior perceptual quality of our results. The code is available at https://github.com/dvlab-research/SNR-Aware-Low-Light-Enhance.
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Cites
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Swin Transformer: Hierarchical Vision Transformer using Shifted Windows
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Cited by
Dancing in the Dark: A Benchmark towards General Low-light Video Enhancement
Fu, H., Zheng, W., Wang, X., Wang, J., Zhang, H., & Ma, H. (2023) · 2023 IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv51070.2023.01183 · Cited by 33
Metadata
- Authors
- Xu, X., Wang, R., Fu, C.-W., & Jia, J.
- Venue
- 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
- Publisher
- IEEE
- Published
- 2022-06
- Pages
- 17693-17703
- ISSN
- 1063-6919 (ISSN-L)
- OpenAlex
- W4313059954
- Cited by
- 688
- References
- 60
- FWCI
- 49.35
- Type
- conference paper
- Language
- en
- Open access
- closed
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