When Multi-Focus Image Fusion Networks Meet Traditional Edge-Preservation Technology-Reference-Cited by-同舟云学术

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When Multi-Focus Image Fusion Networks Meet Traditional Edge-Preservation Technology

Published:2023-06-09 Issue:10 Volume:131 Page:2529-2552
ISSN:0920-5691
Container-title:International Journal of Computer Vision
language:en
Short-container-title:Int J Comput Vis

Author:

Wang Zeyu,Li Xiongfei,Zhao Libo,Duan Haoran,Wang Shidong,Liu Hao,Zhang Xiaoli^ORCID

Funder

National Natural Science Foundation of China

National Key Research and Development Project of China

Thirteenth Five-Year Plan Scientific Research Planning Project of Education Department of Jilin Province

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/s11263-023-01806-w.pdf

Reference54 articles.

1. Agustsson, E., Timofte, R. (2017). Ntire 2017 challenge on single image super-resolution: Dataset and study. In: The IEEE conference on computer vision and pattern recognition (CVPR) Workshops, pp 1–8.

2. Bai, X., Zhang, Y., Zhou, F., et al. (2015). Quadtree-based multi-focus image fusion using a weighted focus-measure. Information Fusion, 22, 105–118.

3. Barash, D. (2002). Fundamental relationship between bilateral filtering, adaptive smoothing, and the nonlinear diffusion equation. IEEE Transactions on Pattern Analysis and Machine Intelligence, 24(6), 844–847.

4. Bhat, S., & Koundal, D. (2021). Multi-focus image fusion techniques: a survey. Artificial Intelligence Review, 54(8), 5735–5787.

5. Chen, Y., & Blum, R. S. (2009). A new automated quality assessment algorithm for image fusion. Image and Vision Computing, 27(10), 1421–1432.

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