Rician noise removal via weighted nuclear norm penalization

DOI10.1016/j.acha.2020.12.005zbMath1469.65107OpenAlexW3125590956MaRDI QIDQ2036417

Jiapeng Tian, Jian Lu, Qingtang Jiang, Zhenwei Hu, Xiaoxia Liu, Yuru Zou

Publication date: 29 June 2021

Published in: Applied and Computational Harmonic Analysis (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.acha.2020.12.005

zbMATH Keywords

low rank approximationweighted nuclear normRician noise removal

Mathematics Subject Classification ID

Numerical mathematical programming methods (65K05) Numerical aspects of computer graphics, image analysis, and computational geometry (65D18) Image processing (compression, reconstruction, etc.) in information and communication theory (94A08)

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Cites Work

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• Linearized alternating direction method with adaptive penalty and warm starts for fast solving transform invariant low-rank textures
• \(\ell _p\) regularized low-rank approximation via iterative reweighted singular value minimization
• Weighted nuclear norm minimization and its applications to low level vision
• A convex variational model for restoring blurred images with large Rician noise
• Nonsmooth analysis of singular values. II: Applications
• Exact matrix completion via convex optimization
• A Singular Value Thresholding Algorithm for Matrix Completion
• A Variational Approach to Removing Multiplicative Noise
• Sparse Reconstruction by Separable Approximation