A New Image Denoising Method Combining the Nonsubsampled 

Contourlet Transform and Adaptive Total Variation

Wu Xiao-yue; Guo Bao-long; Li Lei-da

doi:10.3724/SP.J.1146.2008.01830

Volume 32 Issue 2

Aug. 2010

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Article Navigation > Journal of Electronics & Information Technology > 2010 > 32(2): 360-365

Wu Xiao-yue, Guo Bao-long, Li Lei-da. A New Image Denoising Method Combining the Nonsubsampled Contourlet Transform and Adaptive Total Variation[J]. Journal of Electronics & Information Technology, 2010, 32(2): 360-365. doi: 10.3724/SP.J.1146.2008.01830

Citation:

Wu Xiao-yue, Guo Bao-long, Li Lei-da. A New Image Denoising Method Combining the Nonsubsampled Contourlet Transform and Adaptive Total Variation[J]. Journal of Electronics & Information Technology, 2010, 32(2): 360-365. doi: 10.3724/SP.J.1146.2008.01830

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A New Image Denoising Method Combining the Nonsubsampled Contourlet Transform and Adaptive Total Variation

doi: 10.3724/SP.J.1146.2008.01830

Received Date: 2008-12-30
Rev Recd Date: 2009-10-22
Publish Date: 2010-02-19

Abstract

Abstract

This paper presents a new image denoising scheme by combining the NonSubsampled Contourlet Transform (NSCT) and adaptive total variation model. The original image is first decomposed using NSCT and the image model is built based on Gaussian Scale Mixtures (GSM) model. Then the image noises are removed using Bayesian estimation, producing the preliminary denoised image after reconstruction. Then the preliminary primary denoised image is further filtered using the adaptive total variation model, producing the final denoised image. Experiments show that the proposed scheme can remove Gibbs-like artifacts and image noise effectively. Besides, it outperforms the existing schemes in regard of both the Peak-Signal-to-Noise-Ratio (PSNR) and the edge preservation ability.

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References

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