Nonlocal Low-Rank Regularization Combined with Bilateral Total Variation for Compressive Sensing Image Reconstruction
The use of non-local self-similarity prior between image blocks can improve image reconstruction performance significantly. We propose a compressive sensing image reconstruction algorithm that combines bilateral total variation and nonlocal low-rank regularization to overcome over-smoothing and degr...
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| Format: | Article |
| Language: | English |
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MDPI AG
2021-02-01
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| Series: | Electronics |
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| Online Access: | https://www.mdpi.com/2079-9292/10/4/385 |
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| author | Kunhao Zhang Yali Qin Huan Zheng Hongliang Ren Yingtian Hu |
| author_facet | Kunhao Zhang Yali Qin Huan Zheng Hongliang Ren Yingtian Hu |
| author_sort | Kunhao Zhang |
| collection | DOAJ |
| description | The use of non-local self-similarity prior between image blocks can improve image reconstruction performance significantly. We propose a compressive sensing image reconstruction algorithm that combines bilateral total variation and nonlocal low-rank regularization to overcome over-smoothing and degradation of edge information which result from the prior reconstructed image. The proposed algorithm makes use of the preservation of image edge information by bilateral total variation operator to enhance the edge details of the reconstructed image. In addition, we use weighted nuclear norm regularization as a low-rank constraint for similar blocks of the image. To solve this convex optimization problem, the Alternating Direction Method of Multipliers (ADMM) is employed to optimize and iterate the algorithm model effectively. Experimental results show that the proposed algorithm can obtain better image reconstruction quality than conventional algorithms with using total variation regularization or considering the nonlocal structure of the image only. At 10% sampling rate, the peak signal-to-noise ratio gain is up to 2.39 dB in noiseless measurements compared with Nonlocal Low-rank Regularization (NLR-CS). Reconstructed image comparison shows that the proposed algorithm retains more high frequency components. In noisy measurements, the proposed algorithm is robust to noise and the reconstructed image retains more detail information. |
| first_indexed | 2024-03-09T05:37:35Z |
| format | Article |
| id | doaj.art-4445e14c4d1c458c831ae526652aa56f |
| institution | Directory Open Access Journal |
| issn | 2079-9292 |
| language | English |
| last_indexed | 2024-03-09T05:37:35Z |
| publishDate | 2021-02-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Electronics |
| spelling | doaj.art-4445e14c4d1c458c831ae526652aa56f2023-12-03T12:27:09ZengMDPI AGElectronics2079-92922021-02-0110438510.3390/electronics10040385Nonlocal Low-Rank Regularization Combined with Bilateral Total Variation for Compressive Sensing Image ReconstructionKunhao Zhang0Yali Qin1Huan Zheng2Hongliang Ren3Yingtian Hu4Institute of Fiber-Optic Communication and Information Engineering, College of Information Engineering, Zhejiang University of Technology, Hangzhou 310023, ChinaInstitute of Fiber-Optic Communication and Information Engineering, College of Information Engineering, Zhejiang University of Technology, Hangzhou 310023, ChinaInstitute of Fiber-Optic Communication and Information Engineering, College of Information Engineering, Zhejiang University of Technology, Hangzhou 310023, ChinaInstitute of Fiber-Optic Communication and Information Engineering, College of Information Engineering, Zhejiang University of Technology, Hangzhou 310023, ChinaInstitute of Fiber-Optic Communication and Information Engineering, College of Information Engineering, Zhejiang University of Technology, Hangzhou 310023, ChinaThe use of non-local self-similarity prior between image blocks can improve image reconstruction performance significantly. We propose a compressive sensing image reconstruction algorithm that combines bilateral total variation and nonlocal low-rank regularization to overcome over-smoothing and degradation of edge information which result from the prior reconstructed image. The proposed algorithm makes use of the preservation of image edge information by bilateral total variation operator to enhance the edge details of the reconstructed image. In addition, we use weighted nuclear norm regularization as a low-rank constraint for similar blocks of the image. To solve this convex optimization problem, the Alternating Direction Method of Multipliers (ADMM) is employed to optimize and iterate the algorithm model effectively. Experimental results show that the proposed algorithm can obtain better image reconstruction quality than conventional algorithms with using total variation regularization or considering the nonlocal structure of the image only. At 10% sampling rate, the peak signal-to-noise ratio gain is up to 2.39 dB in noiseless measurements compared with Nonlocal Low-rank Regularization (NLR-CS). Reconstructed image comparison shows that the proposed algorithm retains more high frequency components. In noisy measurements, the proposed algorithm is robust to noise and the reconstructed image retains more detail information.https://www.mdpi.com/2079-9292/10/4/385compressive sensingcomputational imagingbilateral total variationweighted nuclear normnonlocal self-similarity |
| spellingShingle | Kunhao Zhang Yali Qin Huan Zheng Hongliang Ren Yingtian Hu Nonlocal Low-Rank Regularization Combined with Bilateral Total Variation for Compressive Sensing Image Reconstruction Electronics compressive sensing computational imaging bilateral total variation weighted nuclear norm nonlocal self-similarity |
| title | Nonlocal Low-Rank Regularization Combined with Bilateral Total Variation for Compressive Sensing Image Reconstruction |
| title_full | Nonlocal Low-Rank Regularization Combined with Bilateral Total Variation for Compressive Sensing Image Reconstruction |
| title_fullStr | Nonlocal Low-Rank Regularization Combined with Bilateral Total Variation for Compressive Sensing Image Reconstruction |
| title_full_unstemmed | Nonlocal Low-Rank Regularization Combined with Bilateral Total Variation for Compressive Sensing Image Reconstruction |
| title_short | Nonlocal Low-Rank Regularization Combined with Bilateral Total Variation for Compressive Sensing Image Reconstruction |
| title_sort | nonlocal low rank regularization combined with bilateral total variation for compressive sensing image reconstruction |
| topic | compressive sensing computational imaging bilateral total variation weighted nuclear norm nonlocal self-similarity |
| url | https://www.mdpi.com/2079-9292/10/4/385 |
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