New robust principal component analysis for joint image alignment and recovery via affine transformations, Frobenius and \(L_{2,1}\) norms

DOI10.1155/2020/8136384zbMath1486.90145OpenAlexW3015585990MaRDI QIDQ779978

Habte Tadesse Likassa

Publication date: 14 July 2020

Published in: International Journal of Mathematics and Mathematical Sciences (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1155/2020/8136384

zbMATH Keywords

\(L_{2,1}\) normsFrobenius normsmatrix recovery problem

Mathematics Subject Classification ID

Convex programming (90C25) Image processing (compression, reconstruction, etc.) in information and communication theory (94A08) Numerical methods for low-rank matrix approximation; matrix compression (65F55)

Related Items (2)

New robust regularized shrinkage regression for high-dimensional image recovery and alignment via affine transformation and Tikhonov regularization ⋮ New robust PCA for outliers and heavy sparse noises' detection via affine transformation, the \(L_{\ast, w}\) and \(L_{2,1}\) norms, and spatial weight matrix in high-dimensional images: from the perspective of signal processing

Uses Software

• LFW
• RASL

Cites Work

• Unnamed Item
• Distributed Optimization and Statistical Learning via the Alternating Direction Method of Multipliers
• Weighted nuclear norm minimization and its applications to low level vision
• A Novel M-Estimator for Robust PCA
• A Singular Value Thresholding Algorithm for Matrix Completion
• A Fast Algorithm for Edge-Preserving Variational Multichannel Image Restoration
• Estimation of Subspace Arrangements with Applications in Modeling and Segmenting Mixed Data
• Robust PCA Based on Incoherence With Geometrical Interpretation
• Randomized Dynamic Mode Decomposition
• Robust Matrix Decomposition With Sparse Corruptions

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