An inexact proximal majorization-minimization algorithm for remote sensing image stripe noise removal-Reference-Cited by-同舟云学术

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An inexact proximal majorization-minimization algorithm for remote sensing image stripe noise removal

Published:2024-01-12 Issue: Volume: Page:
ISSN:1017-1398
Container-title:Numerical Algorithms
language:en
Short-container-title:Numer Algor

Author:

Wang Chengjing,Zhao Xile,Wang Qingsong,Ma Zepei,Tang Peipei

Funder

National Natural Science Foundation of China

Zhejiang Provincial Natural Science Foundation of China

National Key Research and Development Program of China

Open Research Fund Program of Data Recovery Key Laboratory of Sichuan Province

Scientific Research Foundation of Zhejiang University City College

Publisher

Springer Science and Business Media LLC

Link

https://link.springer.com/content/pdf/10.1007/s11075-023-01743-2.pdf

Reference21 articles.

1. Attouch, H., Bolte, J.: On the convergence of the proximal algorithm for nonsmooth functions involving analytic features. Math. Program. 116, 5–16 (2009)

2. Attouch, H., Bolte, J., Svaiter, B.: Convergence of descent methods for semi-algebraic and tame problems: proximal algorithms, forward-backward splitting, and regularized Gauss-Seidel methods. Math. Program. 137, 91–129 (2013)

3. Bolte, J., Pauwels, E.: Majorization-minimization procedures and convergence of SQP methods for semi-algebraic and tame programs. Math. Oper. Res. 41(2), 442–465 (2016)

4. Bouali, M., Ladjal, S.: Toward optimal destriping of MODIS data using a unidirectional variational model. IEEE Trans. Geosci. Remote Sens. 49(8), 2924–2935 (2011)

5. Chen, L., Sun, D.F., Toh, K.C.: An efficient inexact symmetric Gauss-Seidel based majorized ADMM for high-dimensional convex composite conic programming. Math. Program. 161, 237–270 (2017)

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