Image Denoising Based on Nonsubsampled Contourlet Transform and Bivariate Model

Bian Ce; Zhong Hua; Jiao Li-cheng

doi:10.3724/SP.J.1146.2007.01636

Volume 31 Issue 3

Dec. 2010

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Bian Ce, Zhong Hua, Jiao Li-cheng. Image Denoising Based on Nonsubsampled Contourlet Transform and Bivariate Model[J]. Journal of Electronics & Information Technology, 2009, 31(3): 561-565. doi: 10.3724/SP.J.1146.2007.01636

Citation:

Bian Ce, Zhong Hua, Jiao Li-cheng. Image Denoising Based on Nonsubsampled Contourlet Transform and Bivariate Model[J]. Journal of Electronics & Information Technology, 2009, 31(3): 561-565. doi: 10.3724/SP.J.1146.2007.01636

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Image Denoising Based on Nonsubsampled Contourlet Transform and Bivariate Model

doi: 10.3724/SP.J.1146.2007.01636

Received Date: 2007-10-16
Rev Recd Date: 2008-06-23
Publish Date: 2009-03-19

Abstract

Abstract

This paper proposes a new image denoising method based on the NonsubSampled Contourlet Transform(NSCT) and the bivariate model under the framework of Bayesian MAP estimation theory. The proposed algorithm uses the NSCTs advantages of translation-invariant and multidirection-selectivity, exploits the intra-scale and inter-scale correlations of NSCT coefficients, and elaborates the method of noise estimation. Compared with some current outstanding denoising methods, the simulation results and analysis show that the proposed algorithm obviously outperforms in both Peak Signal-to-Noise Ratio(PSNR) and visual quality, and effectively preserves detail and texture information of original images.

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References(1)

References

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