Single image super-resolution using dictionary-based local regression

Sundaresh Ram, Jeffrey J. Rodríguez

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

10 Scopus citations

Abstract

This paper presents a new method of producing a high-resolution image from a single low-resolution image without any external training image sets. We use a dictionary-based regression model for practical image super-resolution using local self-similar example patches within the image. Our method is inspired by the observation that image patches can be well represented as a sparse linear combination of elements from a chosen over-complete dictionary and that a patch in the high-resolution image have good matches around its corresponding location in the low-resolution image. A first-order approximation of a nonlinear mapping function, learned using the local self-similar example patches, is applied to the low-resolution image patches to obtain the corresponding high-resolution image patches. We show that the proposed algorithm provides improved accuracy compared to the existing single image super-resolution methods by running them on various input images that contain diverse textures, and that are contaminated by noise or other artifacts.

Original languageEnglish (US)
Title of host publication2014 IEEE Southwest Symposium on Image Analysis and Interpretation, SSIAI 2014 - Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages121-124
Number of pages4
ISBN (Print)9781479940530
DOIs
StatePublished - Jan 1 2014
Event2014 IEEE Southwest Symposium on Image Analysis and Interpretation, SSIAI 2014 - San Diego, CA, United States
Duration: Apr 6 2014Apr 8 2014

Publication series

NameProceedings of the IEEE Southwest Symposium on Image Analysis and Interpretation

Other

Other2014 IEEE Southwest Symposium on Image Analysis and Interpretation, SSIAI 2014
CountryUnited States
CitySan Diego, CA
Period4/6/144/8/14

Keywords

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

ASJC Scopus subject areas

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

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