Image Denoising Using Neighbouring Contourlet Coefficients

Authors:
Guangyi Chen;Wei-Ping Zhu
Affiliations:
Department of Electrical and Computer Engineering, Concordia University, Montreal, Canada H3G 1M8;Department of Electrical and Computer Engineering, Concordia University, Montreal, Canada H3G 1M8
Venue:
ISNN '08 Proceedings of the 5th international symposium on Neural Networks: Advances in Neural Networks, Part II
Year:
2008

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Abstract

The denoising of a natural image corrupted by Gaussian white noise is a classical problem in image processing. In this paper, a new image denoising method is proposed by using the contourlet transform. The thresholding process employs a small neighbourhood for the current contourlet coefficient to be thresholded. This is because the contourlet coefficients are correlated, and large contourlet coefficients will normally have large coefficients at its neighbour locations. Experiments show that the proposed method is better than the standard contourlet denoising and the wavelet denoising.