Block Compressed Sensing of Images Using Adaptive Granular Reconstruction
In the framework of block Compressed Sensing (CS), the reconstruction algorithm based on the Smoothed Projected Landweber (SPL) iteration can achieve the better rate-distortion performance with a low computational complexity, especially for using the Principle Components Analysis (PCA) to perform th...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2016-01-01
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| Series: | Advances in Multimedia |
| Online Access: | http://dx.doi.org/10.1155/2016/1280690 |
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| author | Ran Li Hongbing Liu Yu Zeng Yanling Li |
| author_facet | Ran Li Hongbing Liu Yu Zeng Yanling Li |
| author_sort | Ran Li |
| collection | DOAJ |
| description | In the framework of block Compressed Sensing (CS), the reconstruction algorithm based on the Smoothed Projected Landweber (SPL) iteration can achieve the better rate-distortion performance with a low computational complexity, especially for using the Principle Components Analysis (PCA) to perform the adaptive hard-thresholding shrinkage. However, during learning the PCA matrix, it affects the reconstruction performance of Landweber iteration to neglect the stationary local structural characteristic of image. To solve the above problem, this paper firstly uses the Granular Computing (GrC) to decompose an image into several granules depending on the structural features of patches. Then, we perform the PCA to learn the sparse representation basis corresponding to each granule. Finally, the hard-thresholding shrinkage is employed to remove the noises in patches. The patches in granule have the stationary local structural characteristic, so that our method can effectively improve the performance of hard-thresholding shrinkage. Experimental results indicate that the reconstructed image by the proposed algorithm has better objective quality when compared with several traditional ones. The edge and texture details in the reconstructed image are better preserved, which guarantees the better visual quality. Besides, our method has still a low computational complexity of reconstruction. |
| format | Article |
| id | doaj-art-048c72d2058f424fa3d1bda8f240372c |
| institution | Kabale University |
| issn | 1687-5680 1687-5699 |
| language | English |
| publishDate | 2016-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Advances in Multimedia |
| spelling | doaj-art-048c72d2058f424fa3d1bda8f240372c2025-02-03T05:50:12ZengWileyAdvances in Multimedia1687-56801687-56992016-01-01201610.1155/2016/12806901280690Block Compressed Sensing of Images Using Adaptive Granular ReconstructionRan Li0Hongbing Liu1Yu Zeng2Yanling Li3School of Computer and Information Technology, Xinyang Normal University, Xinyang 464000, ChinaSchool of Computer and Information Technology, Xinyang Normal University, Xinyang 464000, ChinaSchool of Computer and Information Technology, Xinyang Normal University, Xinyang 464000, ChinaSchool of Computer and Information Technology, Xinyang Normal University, Xinyang 464000, ChinaIn the framework of block Compressed Sensing (CS), the reconstruction algorithm based on the Smoothed Projected Landweber (SPL) iteration can achieve the better rate-distortion performance with a low computational complexity, especially for using the Principle Components Analysis (PCA) to perform the adaptive hard-thresholding shrinkage. However, during learning the PCA matrix, it affects the reconstruction performance of Landweber iteration to neglect the stationary local structural characteristic of image. To solve the above problem, this paper firstly uses the Granular Computing (GrC) to decompose an image into several granules depending on the structural features of patches. Then, we perform the PCA to learn the sparse representation basis corresponding to each granule. Finally, the hard-thresholding shrinkage is employed to remove the noises in patches. The patches in granule have the stationary local structural characteristic, so that our method can effectively improve the performance of hard-thresholding shrinkage. Experimental results indicate that the reconstructed image by the proposed algorithm has better objective quality when compared with several traditional ones. The edge and texture details in the reconstructed image are better preserved, which guarantees the better visual quality. Besides, our method has still a low computational complexity of reconstruction.http://dx.doi.org/10.1155/2016/1280690 |
| spellingShingle | Ran Li Hongbing Liu Yu Zeng Yanling Li Block Compressed Sensing of Images Using Adaptive Granular Reconstruction Advances in Multimedia |
| title | Block Compressed Sensing of Images Using Adaptive Granular Reconstruction |
| title_full | Block Compressed Sensing of Images Using Adaptive Granular Reconstruction |
| title_fullStr | Block Compressed Sensing of Images Using Adaptive Granular Reconstruction |
| title_full_unstemmed | Block Compressed Sensing of Images Using Adaptive Granular Reconstruction |
| title_short | Block Compressed Sensing of Images Using Adaptive Granular Reconstruction |
| title_sort | block compressed sensing of images using adaptive granular reconstruction |
| url | http://dx.doi.org/10.1155/2016/1280690 |
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