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Single Image Super Resolution Reconstruction in Perturbed Exemplar Sub-space

  • Takashi Shibata
  • Akihiko Iketani
  • Shuji Senda
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7726)

Abstract

This paper presents a novel single image super resolution method that reconstructs a super resolution image in an exemplar sub-space. The proposed method first synthesizes LR patches by perturbing the image formation model, and stores them in a dictionary. An SR image is generated by replacing the input image patchwise with an HR patch in the dictionary whose LR patch best matches the input. The abundance of the exemplars enables the proposed method to synthesize SR images within the exemplar sub-space. This gives numerous advantages over the previous methods, such as the robustness against noise. Experiments on documents images show the proposed method outperforms previous methods not only in image quality, but also in recognition rate, namely about 30% higher than the previous methods.

Keywords

Root Mean Square Error Recognition Rate Point Spread Function Document Image Proposed Method 
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 2013

Authors and Affiliations

  • Takashi Shibata
    • 1
  • Akihiko Iketani
    • 1
  • Shuji Senda
    • 1
  1. 1.NEC CorporationKawasakiJapan

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