Gaussian Patch Mixture Model Guided Low-Rank Covariance Matrix Minimization for Image Denoising-Reference-Cited by-同舟云学术

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Gaussian Patch Mixture Model Guided Low-Rank Covariance Matrix Minimization for Image Denoising

Published:2022-10-06 Issue:4 Volume:15 Page:1601-1622
ISSN:1936-4954
Container-title:SIAM Journal on Imaging Sciences
language:en
Short-container-title:SIAM J. Imaging Sci.

Author:

Guo Jing,Guo Yu,Jin Qiyu^ORCID,Kwok-Po Ng Michael^ORCID,Wang Shuping

Funder

China Scholarship Council

Inner Mongolia Autonomous Region

Inner Mongolia University

National Natural Science Foundation of China

Natural Science Foundation of Inner Mongolia

Hong Kong Research Grant Council GRF

Joint NSFC-RGC

Publisher

Society for Industrial & Applied Mathematics (SIAM)

Subject

Applied Mathematics,General Mathematics

Reference57 articles.

1. On the principal components of sample covariance matrices

2. A Non-Local Algorithm for Image Denoising

3. A Singular Value Thresholding Algorithm for Matrix Completion

4. Minimization of a Detail-Preserving Regularization Functional for Impulse Noise Removal

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1. Quaternion Nuclear Norm Minus Frobenius Norm Minimization for color image reconstruction;Pattern Recognition;2025-02

2. Non-Local and Fully Connected Tensor Network Decomposition for Remote Sensing Image Denoising;Numerical Mathematics: Theory, Methods and Applications;2024-06

3. Hybrid model of tensor sparse representation and total variation regularization for image denoising;Signal Processing;2024-04

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