Open Access

Single-Frame Image Super-resolution through Contourlet Learning

  • CV Jiji1 and
  • Subhasis Chaudhuri1
EURASIP Journal on Advances in Signal Processing20062006:073767

https://doi.org/10.1155/ASP/2006/73767

Received: 26 November 2004

Accepted: 5 April 2005

Published: 9 February 2006

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’ Affiliations

(1)
Department of Electrical Engineering, Indian Institute of Technology Bombay

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Copyright

© Jiji and Chaudhuri 2006