On Single Image Scale-Up Using Sparse-Representations

  • Roman Zeyde
  • Michael Elad
  • Matan Protter
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6920)

Abstract

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.

Keywords

Image Patch Sparse Code Reconstruction Phase Visual Artifact Bicubic Interpolation 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Roman Zeyde
    • 1
  • Michael Elad
    • 1
  • Matan Protter
    • 1
  1. 1.The Computer Science DepartmentTechnion, Israel Institute of TechnologyHaifaIsrael

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