Weighted \(l_p\) norm sparse error constraint based ADMM for image denoising

DOI10.1155/2019/1262171zbMath1435.94096OpenAlexW2944557197MaRDI QIDQ2298027

Jiucheng Xu, Keqiang Xu, Nan Wang, Zhanwei Xu

Publication date: 20 February 2020

Published in: Mathematical Problems in Engineering (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1155/2019/1262171

Mathematics Subject Classification ID

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

Uses Software

• DnCNN
• FFDNet

Cites Work

• An algorithm for total variation minimization and applications
• Weighted nuclear norm minimization and its applications to low level vision
• $rm K$-SVD: An Algorithm for Designing Overcomplete Dictionaries for Sparse Representation
• Group-Based Sparse Representation for Image Restoration
• Multiscale Image Blind Denoising
• Robust Fringe Projection Profilometry via Sparse Representation
• Adaptive Image Denoising by Mixture Adaptation
• Weighted Schatten <inline-formula> <tex-math notation="LaTeX">$p$ </tex-math> </inline-formula>-Norm Minimization for Image Denoising and Background Subtraction
• Relative CNN-RNN: Learning Relative Atmospheric Visibility From Images
• Beyond a Gaussian Denoiser: Residual Learning of Deep CNN for Image Denoising
• Efficient Minimization Method for a Generalized Total Variation Functional
• Nonlocally Centralized Sparse Representation for Image Restoration

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