Exploiting structured sparsity for image deblurring

Haichao Zhang, Yanning Zhang, Thomas S. Huang

Research output: Contribution to journalConference articlepeer-review


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 languageEnglish (US)
Article number6298470
Pages (from-to)616-621
Number of pages6
JournalProceedings - IEEE International Conference on Multimedia and Expo
StatePublished - 2012
Event2012 13th IEEE International Conference on Multimedia and Expo, ICME 2012 - Melbourne, VIC, Australia
Duration: Jul 9 2012Jul 13 2012


  • image deblurring
  • image restoration
  • signal processing
  • structured sparsity

ASJC Scopus subject areas

  • Computer Networks and Communications
  • Computer Science Applications


Dive into the research topics of 'Exploiting structured sparsity for image deblurring'. Together they form a unique fingerprint.

Cite this