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Super-Resolution Using Sub-Band Self-Similarity

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Computer Vision -- ACCV 2014 (ACCV 2014)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 9004))

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Abstract

A popular approach for single image super-resolution (SR) is to use scaled down versions of the given image to build an internal training dictionary of pairs of low resolution (LR) and high resolution (HR) image patches, which is then used to predict the HR image. This self-similarity approach has the advantage of not requiring a separate external training database. However, due to their limited size, internal dictionaries are often inadequate for finding good matches for patches containing complex structures such as textures. Furthermore, the quality of matches found are quite sensitive to factors like patch size (larger patches contain structures of greater complexity and may be difficult to match), and dimensions of the given image (smaller images yield smaller internal dictionaries). In this paper we propose a self-similarity based SR algorithm that addresses the abovementioned drawbacks. Instead of seeking similar patches directly in the image domain, we use the self-similarity principle independently on each of a set of different sub-band images, obtained using a bank of orientation selective band-pass filters. Therefore, we allow the different directional frequency components of a patch to find matches independently, which may be in different image locations. Essentially, we decompose local image structure into component patches defined by different sub-bands, with the following advantages: (1) The sub-band image patches are simpler and therefore easier to find matches, than for the more complex textural patches from the original image. (2) The size of the dictionary defined by patches from the sub-band images is exponential in the number of sub-bands used, thus increasing the effective size of the internal dictionary. (3) As a result, our algorithm exhibits a greater degree of invariance to parameters like patch size and the dimensions of the LR image. We demonstrate these advantages and show that our results are richer in textural content and appear more natural than several state-of-the-art methods.

Electronic supplementary material The online version of this chapter (doi:10.1007/978-3-319-16808-1_37) contains supplementary material, which is available to authorized users.

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Notes

  1. 1.

    The software for many of these methods were provided by the respective authors, while we implemented the others.

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Authors and Affiliations

  1. University of Illinois at Urbana-Champaign, Champaign, USA

    Abhishek Singh & Narendra Ahuja

Authors
  1. Abhishek Singh
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  2. Narendra Ahuja
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Corresponding author

Correspondence to Abhishek Singh .

Editor information

Editors and Affiliations

  1. Technische Universität München, Garching, Bayern, Germany

    Daniel Cremers

  2. University of Adelaide, Adelaide, South Australia, Australia

    Ian Reid

  3. Keio University, Yokohama, Kanagawa, Japan

    Hideo Saito

  4. University of California at Merced, Merced, California, USA

    Ming-Hsuan Yang

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Singh, A., Ahuja, N. (2015). Super-Resolution Using Sub-Band Self-Similarity. In: Cremers, D., Reid, I., Saito, H., Yang, MH. (eds) Computer Vision -- ACCV 2014. ACCV 2014. Lecture Notes in Computer Science(), vol 9004. Springer, Cham. https://doi.org/10.1007/978-3-319-16808-1_37

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  • DOI: https://doi.org/10.1007/978-3-319-16808-1_37

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-16807-4

  • Online ISBN: 978-3-319-16808-1

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