Salt and pepper noise removal with multi-class dictionary learning and L\(_0\) norm regularizations

DOI10.3390/a12010007zbMath1461.94009OpenAlexW2905867788MaRDI QIDQ2633271

Xiaobo Qu, Xiaofeng Du, Min Xiao, Di Guo, Zhangren Tu, Jiechao Wang

Publication date: 8 May 2019

Published in: Algorithms (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.3390/a12010007

zbMATH Keywords

sparse representationdictionary learningimpulse noise removalL\(_0\) norm

Mathematics Subject Classification ID

Signal theory (characterization, reconstruction, filtering, etc.) (94A12) Image processing (compression, reconstruction, etc.) in information and communication theory (94A08)

Related Items (1)

Representation recovery via \(L_1\)-norm minimization with corrupted data

Uses Software

• DLMRI-Lab

Cites Work

• The benefit of group sparsity
• A variational approach to remove outliers and impulse noise
• Weak fault detection of tapered rolling bearing based on penalty regularization approach
• Data-driven tight frame construction and image denoising
• Restoration of images corrupted by mixed Gaussian-impulse noise via \(l_{1}-l_{0}\) minimization
• A modified iterative alternating direction minimization algorithm for impulse noise removal in images
• Image denoising using scale mixtures of gaussians in the wavelet domain
• Group-Sparse Signal Denoising: Non-Convex Regularization, Convex Optimization
• $rm K$-SVD: An Algorithm for Designing Overcomplete Dictionaries for Sparse Representation

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