Open Access

Contourlet Filter Design Based on Chebyshev Best Uniform Approximation

  • Guoan Yang1Email author,
  • Xiaofeng Fang2,
  • Mingli Jing1,
  • Songjun Zhang2 and
  • Ming Hou1
EURASIP Journal on Advances in Signal Processing20102010:398385

Received: 11 June 2009

Accepted: 17 March 2010

Published: 27 April 2010


The contourlet transform can deal effectively with images which have directional information such as contour and texture. In contrast to wavelets for which there exists many good filters, the contourlet filter design for image processing applications is still an ongoing work. Therefore, this paper presents an approach for designing the contourlet filter based on the Chebyshev best uniform approximation for achieving an efficient image denoising applications using hidden Markov tree models in the contourlet domain. Here, we design both the optimal 9/7 wavelet filter banks with rational coefficients and new pkva 12 filter. In this paper, the Laplacian pyramid followed by the direction filter banks decomposition in the contourlet transform using the two filter banks above and the image denoising applications in the contourlet hidden Markov tree model are implemented, respectively. The experimental results show that the denoising performance of the test image Zelda in terms of peak signal-to-noise ratio is improved by 0.33 dB than using CDF 9/7 filter banks with irrational coefficients on the JPEG2000 standard and standard pkva 12 filter, and visual effects are as good as compared with the research results of Duncan D.-Y. Po and Minh N. Do.

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Authors’ Affiliations

School of Electronics and Informations, Xi'an Jiaotong University, Xi'an, China
School of Science, Xi'an Jiaotong University, Xi'an, China


© Guoan Yang et al. 2010

This article is published under license to BioMed Central Ltd. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.