Field of experts regularized nonlocal low rank matrix approximation for image denoising | |
Yang, Hanmei5; Lu J(鲁坚)1; Zhang, Heng6; Luo Y(罗烨)4,5; Lu, Jianwei2,3,5 | |
刊名 | Journal of Computational and Applied Mathematics |
2022 | |
卷号 | 412页码:1-16 |
关键词 | Field of experts Half quadratic splitting Image denoising Nonlocal low rank Weighted nuclear norm |
ISSN号 | 0377-0427 |
产权排序 | 4 |
英文摘要 | The restoration of image degraded by noise is an essential preprocessing step for various imaging technologies. Nonlocal low rank matrix approximation has been successfully applied to image denoising due to the capability of recovering the underlying low rank structures. Unfortunately, existing rank minimization models ignore the correlation among image patches and their performance is degraded when encountering the heavy noise. To address this, we propose a field of experts regularized nonlocal low rank matrix approximation (RFoE) denoising model, which integrates a global field of experts (FoE) regularization, a fidelity term, and a nonlocal low rank constraint into a unified framework. The weighted nuclear norm is adopted as the low rank constraint while the FoE prior is utilized to capture the global structure information. An alternating direction minimization algorithm based on half quadratic splitting can effectively solve this model. Extensive experimental results demonstrate that the proposed RFoE model has a superior performance. |
语种 | 英语 |
资助机构 | General Program of National Natural Science Foundation of China (NSFC) under Grant 61806147 ; Natural Science Foundation of China under grants 61972265 and 11871348 ; Natural Science Foundation of Guangdong Province of China under grant 2020B1515310008 ; Educational Commission of Guangdong Province of China under grant 2019KZDZX1007 ; State Key Laboratory of Robotics, China (2019-O15) |
内容类型 | 期刊论文 |
源URL | [http://ir.sia.cn/handle/173321/30819] |
专题 | 沈阳自动化研究所_其他 |
通讯作者 | Luo Y(罗烨); Lu, Jianwei |
作者单位 | 1.Shenzhen Key Laboratory of Advanced Machine Learning and Applications, College of Mathematics and Statistics, Shenzhen University, Shenzhen 518060, China 2.Engineering Research Center of Traditional Chinese Medicine Intelligent Rehabilitation, Ministry of Education, Shanghai 201203, China 3.College of Rehabilitation Science, Shanghai University of Traditional Chinese Medicine, Shanghai 201203, China 4.State key Laboratory of Robotics, Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang 110169, China 5.School of Software Engineering, Tongji University, Shanghai 201804, China 6.Gaoling School of Artificial Intelligence, Renmin University of China, Beijing 100872, China |
推荐引用方式 GB/T 7714 | Yang, Hanmei,Lu J,Zhang, Heng,et al. Field of experts regularized nonlocal low rank matrix approximation for image denoising[J]. Journal of Computational and Applied Mathematics,2022,412:1-16. |
APA | Yang, Hanmei,Lu J,Zhang, Heng,Luo Y,&Lu, Jianwei.(2022).Field of experts regularized nonlocal low rank matrix approximation for image denoising.Journal of Computational and Applied Mathematics,412,1-16. |
MLA | Yang, Hanmei,et al."Field of experts regularized nonlocal low rank matrix approximation for image denoising".Journal of Computational and Applied Mathematics 412(2022):1-16. |
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