The DOI matches its Crossref record; 26 of the 26 references it cites were found in a registry or an index.
A New Low-Light Image Enhancement Algorithm Using Camera Response Model
Abstract
Low-light images are not conducive to human observation and computer vision algorithms due to their low visibility. To solve this problem, many image enhancement techniques have been proposed. However, existing techniques inevitably introduce color and lightness distortion when increasing visibility. To lower the distortion, we propose a novel enhancement method using the response characteristics of cameras. First, we investigate the relationship between two images with different exposures to obtain an accurate camera response model. Then we borrow the illumination estimation techniques to estimate the exposure ratio map. Finally, we use our camera response model to adjust each pixel to its desired exposure according to the estimated exposure ratio map. Experiments show that our method can obtain enhancement results with less color and lightness distortion compared to several state-of-the-art methods.
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Cites
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Zhu, P., Wen, L., Du, D., Bian, X., Fan, H., Hu, Q., & Ling, H. (2022) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2021.3119563 · Cited by 1,336
Underexposed Photo Enhancement Using Deep Illumination Estimation
Wang, R., Zhang, Q., Fu, C.-W., Shen, X., Zheng, W.-S., & Jia, J. (2019) · 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2019.00701 · Cited by 983
Self-citationSNR-Aware Low-light Image Enhancement
Xu, X., Wang, R., Fu, C.-W., & Jia, J. (2022) · 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr52688.2022.01719 · Cited by 688
Self-citationLow-Light Image Enhancement via a Deep Hybrid Network
Ren, W., Liu, S., Ma, L., Xu, Q., Xu, X., Cao, X., Du, J., & Yang, M.-H. (2019) · IEEE Transactions on Image Processing · doi:10.1109/tip.2019.2910412 · Cited by 508
Attention Guided Low-Light Image Enhancement with a Large Scale Low-Light Simulation Dataset
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Metadata
- Authors
- Ying, Z., Li, G., Ren, Y., Wang, R., & Wang, W.
- Venue
- 2017 IEEE International Conference on Computer Vision Workshops (ICCVW)
- Publisher
- IEEE
- Published
- 2017-10
- Pages
- 3015-3022
- ISSN
- 2473-9936 (ISSN-L)
- OpenAlex
- W2766802800
- Cited by
- 313
- References
- 26
- FWCI
- 5.99
- Type
- conference paper
- Language
- en
- Open access
- closed
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- Ying, Z., Li, G., Ren, Y., Wang, R., & Wang, W. (2017). A New Low-Light Image Enhancement Algorithm Using Camera Response Model. 2017 IEEE International Conference on Computer Vision Workshops (ICCVW). https://doi.org/10.1109/iccvw.2017.356
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