Fully-connected tensor network decomposition and group sparsity for multitemporal images cloud removal-Reference-Cited by-同舟云学术

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Fully-connected tensor network decomposition and group sparsity for multitemporal images cloud removal

Published:2024 Issue:0 Volume:0 Page:0-0
ISSN:1930-8337
Container-title:Inverse Problems and Imaging
language:
Short-container-title:IPI

Author:

Tu Zhihui,Lu Jian¹,Zhu Hong²,Hu Wenyu³,Jiang Qingtang⁴,Ng Michael K.⁵

Affiliation:

1. Shenzhen Key Laboratory of Advanced Machine Learning and Applications, School of Mathematical Sciences, Shenzhen University, Shenzhen 518060, China

2. School of mathematical sciences, Jiangsu University, Zhenjiang 212013, China

3. College of Mathematics and Computer Science, Gannan Normal University, Ganzhou 341000, China

4. Department of Mathematics and Statistics, University of Missouri–St. Louis, St. Louis, MO 63121 USA

5. Department of Mathematics, The University of Hong Kong, Hong Kong, China

Publisher

American Institute of Mathematical Sciences (AIMS)

Reference35 articles.

1. Minimal partitions and image classification using a gradient-free perimeter approximation

2. Proximal Alternating Minimization and Projection Methods for Nonconvex Problems: An Approach Based on the Kurdyka-Łojasiewicz Inequality

3. Convergence of descent methods for semi-algebraic and tame problems: proximal algorithms, forward–backward splitting, and regularized Gauss–Seidel methods

4. Efficient Tensor Completion for Color Image and Video Recovery: Low-Rank Tensor Train

5. Proximal alternating linearized minimization for nonconvex and nonsmooth problems

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