Image Compressive Sensing via Hybrid Nonlocal Sparsity Regularization
This paper focuses on image compressive sensing (CS). As the intrinsic properties of natural images, nonlocal self-similarity and sparse representation have been widely used in various image processing tasks. Most existing image CS methods apply either self-adaptive dictionary (e.g., principle compo...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2020-10-01
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| Series: | Sensors |
| Subjects: | |
| Online Access: | https://www.mdpi.com/1424-8220/20/19/5666 |
| _version_ | 1797551865168134144 |
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| author | Lizhao Li Song Xiao Yimin Zhao |
| author_facet | Lizhao Li Song Xiao Yimin Zhao |
| author_sort | Lizhao Li |
| collection | DOAJ |
| description | This paper focuses on image compressive sensing (CS). As the intrinsic properties of natural images, nonlocal self-similarity and sparse representation have been widely used in various image processing tasks. Most existing image CS methods apply either self-adaptive dictionary (e.g., principle component analysis (PCA) dictionary and singular value decomposition (SVD) dictionary) or fixed dictionary (e.g., discrete cosine transform (DCT), discrete wavelet transform (DWT), and Curvelet) as the sparse basis, while single dictionary could not fully explore the sparsity of images. In this paper, a Hybrid NonLocal Sparsity Regularization (HNLSR) is developed and applied to image compressive sensing. The proposed HNLSR measures nonlocal sparsity in 2D and 3D transform domain simultaneously, and both self-adaptive singular value decomposition (SVD) dictionary and fixed 3D transform are utilized. We use an efficient alternating minimization method to solve the optimization problem. Experimental results demonstrate that the proposed method outperforms existing methods in both objective evaluation and visual quality. |
| first_indexed | 2024-03-10T15:52:10Z |
| format | Article |
| id | doaj.art-d61dedd77c2a487eb0eae4ea49c1fc3f |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-10T15:52:10Z |
| publishDate | 2020-10-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-d61dedd77c2a487eb0eae4ea49c1fc3f2023-11-20T16:01:19ZengMDPI AGSensors1424-82202020-10-012019566610.3390/s20195666Image Compressive Sensing via Hybrid Nonlocal Sparsity RegularizationLizhao Li0Song Xiao1Yimin Zhao2State Key Lab of Integrated Services Networks, Xidian University, Xi’an 710071, ChinaState Key Lab of Integrated Services Networks, Xidian University, Xi’an 710071, ChinaState Key Lab of Integrated Services Networks, Xidian University, Xi’an 710071, ChinaThis paper focuses on image compressive sensing (CS). As the intrinsic properties of natural images, nonlocal self-similarity and sparse representation have been widely used in various image processing tasks. Most existing image CS methods apply either self-adaptive dictionary (e.g., principle component analysis (PCA) dictionary and singular value decomposition (SVD) dictionary) or fixed dictionary (e.g., discrete cosine transform (DCT), discrete wavelet transform (DWT), and Curvelet) as the sparse basis, while single dictionary could not fully explore the sparsity of images. In this paper, a Hybrid NonLocal Sparsity Regularization (HNLSR) is developed and applied to image compressive sensing. The proposed HNLSR measures nonlocal sparsity in 2D and 3D transform domain simultaneously, and both self-adaptive singular value decomposition (SVD) dictionary and fixed 3D transform are utilized. We use an efficient alternating minimization method to solve the optimization problem. Experimental results demonstrate that the proposed method outperforms existing methods in both objective evaluation and visual quality.https://www.mdpi.com/1424-8220/20/19/5666compressive sensingnonlocal self-similaritysparse representation |
| spellingShingle | Lizhao Li Song Xiao Yimin Zhao Image Compressive Sensing via Hybrid Nonlocal Sparsity Regularization Sensors compressive sensing nonlocal self-similarity sparse representation |
| title | Image Compressive Sensing via Hybrid Nonlocal Sparsity Regularization |
| title_full | Image Compressive Sensing via Hybrid Nonlocal Sparsity Regularization |
| title_fullStr | Image Compressive Sensing via Hybrid Nonlocal Sparsity Regularization |
| title_full_unstemmed | Image Compressive Sensing via Hybrid Nonlocal Sparsity Regularization |
| title_short | Image Compressive Sensing via Hybrid Nonlocal Sparsity Regularization |
| title_sort | image compressive sensing via hybrid nonlocal sparsity regularization |
| topic | compressive sensing nonlocal self-similarity sparse representation |
| url | https://www.mdpi.com/1424-8220/20/19/5666 |
| work_keys_str_mv | AT lizhaoli imagecompressivesensingviahybridnonlocalsparsityregularization AT songxiao imagecompressivesensingviahybridnonlocalsparsityregularization AT yiminzhao imagecompressivesensingviahybridnonlocalsparsityregularization |