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Exploiting Self-similarities for Single Frame Super-Resolution

  • Chih-Yuan Yang
  • Jia-Bin Huang
  • Ming-Hsuan Yang
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6494)

Abstract

We propose a super-resolution method that exploits self-similarities and group structural information of image patches using only one single input frame. The super-resolution problem is posed as learning the mapping between pairs of low-resolution and high-resolution image patches. Instead of relying on an extrinsic set of training images as often required in example-based super-resolution algorithms, we employ a method that generates image pairs directly from the image pyramid of one single frame. The generated patch pairs are clustered for training a dictionary by enforcing group sparsity constraints underlying the image patches. Super-resolution images are then constructed using the learned dictionary. Experimental results show the proposed method is able to achieve the state-of-the-art performance.

Keywords

Image Patch Dictionary Learning Image Pyramid Bicubic Interpolation Blur Kernel 
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 2011

Authors and Affiliations

  • Chih-Yuan Yang
    • 1
  • Jia-Bin Huang
    • 1
  • Ming-Hsuan Yang
    • 1
  1. 1.Electrical Engineering and Computer ScienceUniversity of CaliforniaMercedUSA

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