Enhancing low-light images via skip cross-attention fusion and multi-scale lightweight transformer-Reference-Cited by-同舟云学术

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Enhancing low-light images via skip cross-attention fusion and multi-scale lightweight transformer

Published:2024-02-27 Issue:2 Volume:21 Page:
ISSN:1861-8200
Container-title:Journal of Real-Time Image Processing
language:en
Short-container-title:J Real-Time Image Proc

Author:

Zhang Jianming^ORCID,Xing Zi,Wu Mingshuang,Gui Yan,Zheng Bin

Funder

National Natural Science Foundation of China

the Open Fund of Key Laboratory of Safety Control of Bridge Engineering, Ministry of Education

the Postgraduate Scientific Research Innovation Fund of Changsha University of Science and Technology

the Scientific Research Fund of Hunan Provincial Education Department

Publisher

Springer Science and Business Media LLC

Link

https://link.springer.com/content/pdf/10.1007/s11554-024-01424-w.pdf

Reference44 articles.

1. Mandal, G., Bhattacharya, D., De, P.: Real-time automotive night-vision system for drivers to inhibit headlight glare of the oncoming vehicles and enhance road visibility. J. Real-Time Image Proc. 18, 2193–2209 (2021)

2. Guo, X., Li, Y., Ling, H.: Lime low-light image enhancement via illumination map estimation. IEEE Trans. Image Process. 26(2), 982–993 (2016)

3. Rahman, Z., Pu, Y.F., Aamir, M., Wali, S.: Structure revealing of low-light images using wavelet transform based on fractional-order denoising and multiscale decomposition. Vis. Comput. 37(5), 865–880 (2021)

4. Liu, R., Ma, L., Zhang, J., Fan, X., Luo, Z.: Retinex-inspired unrolling with cooperative prior architecture search for low-light image enhancement. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 10561–10570 (2021)

5. Wu, W., Weng, J., Zhang, P., Wang, X., Yang, W., Jiang, J.: Uretinex-net: Retinex-based deep unfolding network for low-light image enhancement. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 5901–5910 (2022)

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