A Diffusion Equation for Improving the Robustness of Deep Learning Speckle Removal Model-Reference-Cited by-同舟云学术

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A Diffusion Equation for Improving the Robustness of Deep Learning Speckle Removal Model

Published:2024-06-08 Issue:5 Volume:66 Page:801-821
ISSN:0924-9907
Container-title:Journal of Mathematical Imaging and Vision
language:en
Short-container-title:J Math Imaging Vis

Author:

Cheng Li,Xing Yuming,Li Yao,Guo Zhichang

Funder

National Natural Science Foundation of China

Fundamental Research Funds for the Central Universities

Natural Sciences Foundation of Heilongiiang Province

Publisher

Springer Science and Business Media LLC

Link

https://link.springer.com/content/pdf/10.1007/s10851-024-01199-6.pdf

Reference52 articles.

1. Aubert, G., Aujol, J.F.: A variational approach to removing multiplicative noise. SIAM J. Appl. Math. 68(4), 925–946 (2008)

2. Busse, L., Crimmins, T., Fienup, J.: A model based approach to improve the performance of the geometric filtering speckle reduction algorithm. In: 1995 IEEE Ultrasonics Symposium. Proceedings. An International Symposium, vol. 2, pp. 1353–1356 (1995). https://doi.org/10.1109/ULTSYM.1995.495807

3. Carlini, N., Wagner, D.: Towards evaluating the robustness of neural networks. In: 2017 IEEE Symposium on Security and Privacy (sp), pp. 39–57. IEEE (2017)

4. Catté, F., Lions, P.L., Morel, J.M., Coll, T.: Image selective smoothing and edge detection by nonlinear diffusion. SIAM J. Numer. Anal. 29(1), 182–193 (1992). https://doi.org/10.1137/0729012

5. Chen, Y., Pock, T.: Trainable nonlinear reaction diffusion: a flexible framework for fast and effective image restoration. IEEE Trans. Pattern Anal. Mach. Intell. 39(6), 1256–1272 (2016). https://doi.org/10.1109/TPAMI.2016.2596743

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