Block Compressed Sensing of Images Using Adaptive Granular Reconstruction
Joint Authors
Liu, Hongbing
Li, Ran
Zeng, Yu
Li, Yanling
Source
Issue
Vol. 2016, Issue 2016 (31 Dec. 2016), pp.1-9, 9 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2016-11-28
Country of Publication
Egypt
No. of Pages
9
Main Subjects
Information Technology and Computer Science
Abstract EN
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.
American Psychological Association (APA)
Li, Ran& Liu, Hongbing& Zeng, Yu& Li, Yanling. 2016. Block Compressed Sensing of Images Using Adaptive Granular Reconstruction. Advances in Multimedia،Vol. 2016, no. 2016, pp.1-9.
https://search.emarefa.net/detail/BIM-1095300
Modern Language Association (MLA)
Li, Ran…[et al.]. Block Compressed Sensing of Images Using Adaptive Granular Reconstruction. Advances in Multimedia No. 2016 (2016), pp.1-9.
https://search.emarefa.net/detail/BIM-1095300
American Medical Association (AMA)
Li, Ran& Liu, Hongbing& Zeng, Yu& Li, Yanling. Block Compressed Sensing of Images Using Adaptive Granular Reconstruction. Advances in Multimedia. 2016. Vol. 2016, no. 2016, pp.1-9.
https://search.emarefa.net/detail/BIM-1095300
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1095300