RDMA: low-light image enhancement based on retinex decomposition and multi-scale adjustment-Reference-Cited by-同舟云学术

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RDMA: low-light image enhancement based on retinex decomposition and multi-scale adjustment

Published:2023-10-16 Issue: Volume: Page:
ISSN:1868-8071
Container-title:International Journal of Machine Learning and Cybernetics
language:en
Short-container-title:Int. J. Mach. Learn. & Cyber.

Author:

Li Jiafeng,Hao Shuai,Li Tianshuo,Zhuo Li,Zhang Jing

Funder

Natural Science Foundation of Beijing Municipality

Beijing Municipal Education Commission

National Natural Science Foundation of China

Publisher

Springer Science and Business Media LLC

Subject

Artificial Intelligence,Computer Vision and Pattern Recognition,Software

Link

https://link.springer.com/content/pdf/10.1007/s13042-023-01991-7.pdf

Reference43 articles.

1. Wang L, Wang T, Yang D, Fang X, Wan S (2022) Near-infrared fusion for deep lightness enhancement. Int J Mach Learn Cybern 14(5):1–13

2. Liu C, Wu F, Wang X (2022) Efinet: restoration for low-light images via enhancement-fusion iterative network. IEEE Transactions on Circuits and Systems for Video Technology 32(12):8486–8499

3. Xu J, Yuan M, Yan D-M, Wu T (2022) Illumination guided attentive wavelet network for low-light image enhancement. IEEE Transactions on Multimedia

4. Lu Y, Jung S-W (2022) Progressive joint low-light enhancement and noise removal for raw images. IEEE Trans Image Process 31:2390–2404

5. Wan R, Shi B, Yang W, Wen B, Duan LY, Kot AC (2022) Purifying low-light images via near-infrared enlightened image. IEEE Transactions on Multimedia

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