Paper Title:
Image Denoising Algorithm Based on Dyadic Contourlet Transform
  Abstract

This paper constructs a dyadic non-subsampled Contourlet transform for denoising on the image, the transformation has more directional subband, using the non-subsampled filter group for decompositing of direction, so has the translation invariance, eliminated image distortion from Contourlet transform’s lack of translation invariance. Non-subsampled filter reduces noise interference and data redundancy. Using the feature of NSCT translation invariance, multiresolution, multi-direction, and can according to the energy of NSCT in all directions and in all scale, adaptive denoising threshold. Experimental results show that compared to wavelet denoising and traditional Contourlet denoising, the method achieves a higher PSNR value, while preserving image edge details, can effectively reduce the Gibbs distortion, improve visual images.

  Info
Periodical
Edited by
Zhu Zhilin & Patrick Wang
Pages
591-597
DOI
10.4028/www.scientific.net/AMM.40-41.591
Citation
F. Hui, Y. L. Wang, J. J. Li, "Image Denoising Algorithm Based on Dyadic Contourlet Transform", Applied Mechanics and Materials, Vols. 40-41, pp. 591-597, 2011
Online since
November 2010
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