TY - JOUR
T1 - An Efficient Collaborative Filtering Method for Image Noise and Artifact Removal
AU - Liu, Xuya
AU - Wang, Shumei
AU - Fu, Shujun
AU - Yuliang, Li
AU - Liu, Shouyi
AU - Zhou, Weifeng
N1 - Funding Information:
This work was supported in part by the National Natural Science Foundation of China under Grant 61671276 and Grant 11971269, in part by the Natural Science Foundation of Shandong Province of China under Grant ZR2019MF045, in part by the Qingdao Source Innovation Qroject under Grant 18-2-2-64-jch, and in part by the Science and Technology Program of Universities of Shandong Province under Grant J18KA314.
Publisher Copyright:
© 2013 IEEE.
PY - 2020
Y1 - 2020
N2 - In recent years, sparse representation theory and low-rank approximation model have been widely used in signal and image processing fields. In the study of natural image denoising, non-local similarity method can enhance the correlation of grouped image blocks, and a low rank prior is used to devise a bilateral sparse representation of the image matrix, consequently achieving the purpose of removing additive white Gaussian noise. However, problems such as how to select thresholds after a singular value shrinkage, and how to eliminate image artifacts in removing noise especially with high levels, have not been resolved. In this paper, a low rank adaptive singular value thresholding (ASVT) denoising algorithm based on singular value decomposition(SVD) is proposed. Our method uses random matrix and asymptotic matrix reconstruction theory to scientifically select the threshold of singular value thresholding. At the same time, a dual-domain filtering method is used to process the visual artifacts after image denoising by ASVT, which we call collaborative singular value thresholding (CSVT) algorithm. The experimental results show that the proposed algorithm has a certain improvement in subjective visual effects, and objective quantitative indicators compared with some related advanced denoising algorithms.
AB - In recent years, sparse representation theory and low-rank approximation model have been widely used in signal and image processing fields. In the study of natural image denoising, non-local similarity method can enhance the correlation of grouped image blocks, and a low rank prior is used to devise a bilateral sparse representation of the image matrix, consequently achieving the purpose of removing additive white Gaussian noise. However, problems such as how to select thresholds after a singular value shrinkage, and how to eliminate image artifacts in removing noise especially with high levels, have not been resolved. In this paper, a low rank adaptive singular value thresholding (ASVT) denoising algorithm based on singular value decomposition(SVD) is proposed. Our method uses random matrix and asymptotic matrix reconstruction theory to scientifically select the threshold of singular value thresholding. At the same time, a dual-domain filtering method is used to process the visual artifacts after image denoising by ASVT, which we call collaborative singular value thresholding (CSVT) algorithm. The experimental results show that the proposed algorithm has a certain improvement in subjective visual effects, and objective quantitative indicators compared with some related advanced denoising algorithms.
KW - dual-domain filtering
KW - Image denoising
KW - image enhancement
KW - low-rank approximation
KW - random matrix
KW - thresholding optimization
UR - http://www.scopus.com/inward/record.url?scp=85088690424&partnerID=8YFLogxK
U2 - 10.1109/ACCESS.2020.3005024
DO - 10.1109/ACCESS.2020.3005024
M3 - Article
AN - SCOPUS:85088690424
VL - 8
SP - 124158
EP - 124171
JO - IEEE Access
JF - IEEE Access
M1 - 9125940
ER -