A new nonconvex low-rank tensor approximation method with applications to hyperspectral images denoising

DOI10.1088/1361-6420/acc88azbMath1514.94008OpenAlexW4361294183MaRDI QIDQ6042940

Zhihui Tu, Wenyu Hu, Qingtang Jiang, Jian Lu, Huan Pan, Zhaosong Lu, Unnamed Author

Publication date: 4 May 2023

Published in: Inverse Problems (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1088/1361-6420/acc88a

zbMATH Keywords

nonconvex optimizationdenoisingmixed noiselow-rank tensor approximationhyperspectral image restoration

Mathematics Subject Classification ID

Nonconvex programming, global optimization (90C26) Image processing (compression, reconstruction, etc.) in information and communication theory (94A08)

Cites Work

• Tensor Decompositions and Applications
• Factorization strategies for third-order tensors
• Convex analysis approach to d. c. programming: Theory, algorithms and applications
• Tensor \(N\)-tubal rank and its convex relaxation for low-rank tensor recovery
• Rician noise removal via weighted nuclear norm penalization
• Weighted nuclear norm minimization and its applications to low level vision
• A novel non-convex low-rank tensor approximation model for hyperspectral image restoration
• Nonconvex Nonsmooth Low Rank Minimization via Iteratively Reweighted Nuclear Norm
• Fusing Hyperspectral and Multispectral Images via Coupled Sparse Tensor Factorization
• Nonlocal low-rank regularized two-phase approach for mixed noise removal
• A NONLOCAL LOW-RANK REGULARIZATION METHOD FOR FRACTAL IMAGE CODING
• A Novel Regularized Model for Third-Order Tensor Completion
• Multiplicative Noise Removal: Nonlocal Low-Rank Model and Its Proximal Alternating Reweighted Minimization Algorithm
• FSIM: A Feature Similarity Index for Image Quality Assessment
• Nonlocal Transform-Domain Filter for Volumetric Data Denoising and Reconstruction

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