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Single-Frame Image Super-resolution through Contourlet Learning

EURASIP Journal on Advances in Signal Processing volume 2006, Article number: 073767 (2006) Cite this article

Abstract

We propose a learning-based, single-image super-resolution reconstruction technique using the contourlet transform, which is capable of capturing the smoothness along contours making use of directional decompositions. The contourlet coefficients at finer scales of the unknown high-resolution image are learned locally from a set of high-resolution training images, the inverse contourlet transform of which recovers the super-resolved image. In effect, we learn the high-resolution representation of an oriented edge primitive from the training data. Our experiments show that the proposed approach outperforms standard interpolation techniques as well as a standard (Cartesian) wavelet-based learning both visually and in terms of the PSNR values, especially for images with arbitrarily oriented edges.

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Authors and Affiliations

  1. Department of Electrical Engineering, Indian Institute of Technology Bombay, Mumbai, 400076, India

    CV Jiji & Subhasis Chaudhuri

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  1. CV Jiji
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  2. Subhasis Chaudhuri
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Open Access This article is distributed under the terms of the Creative Commons Attribution 2.0 International License ( https://creativecommons.org/licenses/by/2.0 ), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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Jiji, C., Chaudhuri, S. Single-Frame Image Super-resolution through Contourlet Learning. EURASIP J. Adv. Signal Process. 2006, 073767 (2006). https://doi.org/10.1155/ASP/2006/73767

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