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.
Original language | English (US) |
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Article number | 6298470 |
Pages (from-to) | 616-621 |
Number of pages | 6 |
Journal | Proceedings - IEEE International Conference on Multimedia and Expo |
DOIs | |
State | Published - 2012 |
Event | 2012 13th IEEE International Conference on Multimedia and Expo, ICME 2012 - Melbourne, VIC, Australia Duration: Jul 9 2012 → Jul 13 2012 |
Keywords
- image deblurring
- image restoration
- signal processing
- structured sparsity
ASJC Scopus subject areas
- Computer Networks and Communications
- Computer Science Applications