Image super-resolution using graph regularized block sparse representation

Sundaresh Ram, Jeffrey J Rodriguez

Research output: Chapter in Book/Report/Conference proceedingConference contribution

4 Scopus citations

Abstract

Recently, patch-based sparse representation has been used as a statistical image modeling technique for various image restoration applications, due to its ability to model well the natural image patches and automatically discover interpretable visual patterns. Standard sparse representation however does not consider the intrinsic and geometric structure present in the data, thereby leading to sub-optimal results. In this paper, we exploit the concept that a signal is block sparse in a given basis-i.e., the non-zero elements occur in clusters of varying sizes-and propose an efficient framework for learning sparse representation modeling of natural images, called graph regularized block sparse representation (GRBSR). The proposed GRBSR is able to sparsely represent natural images in the domain of a block, which enforces the intrinsic local sparsity. We apply the proposed GRBSR to learn a dictionary-based local regression model for super-resolving a single low-resolution image without any external training image sets. We show that the proposed method provides improved performance as compared to the existing single-image super-resolution methods by running them on various input images containing diverse textures or other artifacts.

Original languageEnglish (US)
Title of host publication2016 IEEE Southwest Symposium on Image Analysis and Interpretation, SSIAI 2016 - Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages69-72
Number of pages4
Volume2016-April
ISBN (Electronic)9781467399197
DOIs
StatePublished - Apr 25 2016
EventIEEE Southwest Symposium on Image Analysis and Interpretation, SSIAI 2016 - Santa Fe, United States
Duration: Mar 6 2016Mar 8 2016

Other

OtherIEEE Southwest Symposium on Image Analysis and Interpretation, SSIAI 2016
CountryUnited States
CitySanta Fe
Period3/6/163/8/16

Keywords

  • dictionary learning
  • Image restoration
  • image super-resolution
  • regression
  • sparse representation

ASJC Scopus subject areas

  • Software
  • Computer Vision and Pattern Recognition
  • Computer Science Applications

Fingerprint Dive into the research topics of 'Image super-resolution using graph regularized block sparse representation'. Together they form a unique fingerprint.

  • Cite this

    Ram, S., & Rodriguez, J. J. (2016). Image super-resolution using graph regularized block sparse representation. In 2016 IEEE Southwest Symposium on Image Analysis and Interpretation, SSIAI 2016 - Proceedings (Vol. 2016-April, pp. 69-72). [7459177] Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/SSIAI.2016.7459177