On single image scale-up using sparse-representations

Roman Zeyde, Michael Elad, Matan Protter

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review


This paper deals with the single image scale-up problem using sparse-representation modeling. The goal is to recover an original image from its blurred and down-scaled noisy version. Since this problem is highly ill-posed, a prior is needed in order to regularize it. The literature offers various ways to address this problem, ranging from simple linear space-invariant interpolation schemes (e.g., bicubic interpolation), to spatially-adaptive and non-linear filters of various sorts. We embark from a recently-proposed successful algorithm by Yang et. al. [1,2], and similarly assume a local Sparse-Land model on image patches, serving as regularization. Several important modifications to the above-mentioned solution are introduced, and are shown to lead to improved results. These modifications include a major simplification of the overall process both in terms of the computational complexity and the algorithm architecture, using a different training approach for the dictionary-pair, and introducing the ability to operate without a training-set by boot-strapping the scale-up task from the given low-resolution image. We demonstrate the results on true images, showing both visual and PSNR improvements.

Original languageEnglish
Title of host publicationCurves and Surfaces - 7th International Conference, Curves and Surfaces 2010, Revised Selected Papers
Number of pages20
StatePublished - 2012
Event7th International Conference on Curves and Surfaces, Curves and Surfaces 2010 - Avignon, France
Duration: 24 Jun 201030 Jun 2010

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume6920 LNCS


Conference7th International Conference on Curves and Surfaces, Curves and Surfaces 2010

All Science Journal Classification (ASJC) codes

  • Theoretical Computer Science
  • Computer Science(all)


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