A Shrinkage Learning Approach for Single Image Super-Resolution with Overcomplete Representations

  • Amir Adler
  • Yacov Hel-Or
  • Michael Elad
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6312)


We present a novel approach for online shrinkage functions learning in single image super-resolution. The proposed approach leverages the classical Wavelet Shrinkage denoising technique where a set of scalar shrinkage functions is applied to the wavelet coefficients of a noisy image. In the proposed approach, a unique set of learned shrinkage functions is applied to the overcomplete representation coefficients of the interpolated input image. The super-resolution image is reconstructed from the post-shrinkage coefficients. During the learning stage, the low-resolution input image is treated as a reference high-resolution image and a super-resolution reconstruction process is applied to a scaled-down version of it. The shapes of all shrinkage functions are jointly learned by solving a Least Squares optimization problem that minimizes the sum of squared errors between the reference image and its super-resolution approximation. Computer simulations demonstrate superior performance compared to state-of-the-art results.


Sparse Representation Noisy Image Shrinkage Function Overcomplete Representation Image Degradation Model 
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 2010

Authors and Affiliations

  • Amir Adler
    • 1
  • Yacov Hel-Or
    • 2
  • Michael Elad
    • 1
  1. 1.Computer Science DepartmentThe TechnionHaifaIsrael
  2. 2.Efi Arazi School of Computer ScienceThe Interdisciplinary CenterHerzeliaIsrael

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