DocumentCode :
3296173
Title :
Exploiting Structured Sparsity for Image Deblurring
Author :
Zhang, Haichao ; Zhang, Yanning ; Huang, Thomas S.
fYear :
2012
fDate :
9-13 July 2012
Firstpage :
616
Lastpage :
621
Abstract :
Sparsity is an ubiquitous property exhibited by many natural real-world data such as images, which has been playing an important role in image and multi-media data processing. However, for many data, such as images, the sparsity pattern is not completely random, i.e., there are structures over the sparse coefficients. By exploiting this structure, we can model the data better and may further improve the performance of the recovery algorithm. In this paper, we exploit the structured sparsity of natural images for image deblurring application. Experimental results clearly demonstrate the effectiveness of the proposed approach.
Keywords :
image restoration; image data processing; image deblurring; image recovery algorithm; multimedia data processing; sparse coefficients; structured sparsity pattern; Adaptation models; Estimation; Image restoration; Inverse problems; Kernel; PSNR; Wavelet transforms; image deblurring; image restoration; signal processing; structured sparsity;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Multimedia and Expo (ICME), 2012 IEEE International Conference on
Conference_Location :
Melbourne, VIC
ISSN :
1945-7871
Print_ISBN :
978-1-4673-1659-0
Type :
conf
DOI :
10.1109/ICME.2012.110
Filename :
6298470
Link To Document :
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