Multivariate Shrinkage for Image Denoising in Shearlet Domain

Cheng Zhi Deng

doi:10.4028/www.scientific.net/AMM.239-240.966

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HomeApplied Mechanics and MaterialsApplied Mechanics and Materials Vols. 239-240Multivariate Shrinkage for Image Denoising in...

Multivariate Shrinkage for Image Denoising in Shearlet Domain

Abstract:

A new multivariate threshold function for image denoising in the shearlet transfrom is proposed. The new threshod exploits a multivariate normal inverse gaussian probability density function to model neighboring shearlet coefficients. Under this prior, a multivariate Bayesian shearlet estimator is derived by using the maximum a posteriori rule. Experimental results show that the new method achieves state-of-art performance in terms of peak signal-to-noise ratio (PSNR), structural similarity (SSIM) index and visual quality than existing shearlet-based image denoising methods.

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Periodical:

Applied Mechanics and Materials (Volumes 239-240)

Pages:

966-969

DOI:

https://doi.org/10.4028/www.scientific.net/AMM.239-240.966

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Online since:

December 2012

Authors:

Cheng Zhi Deng

Keywords:

Image De-Noising, Multivariate Statistical Model, Normal Inverse Gaussian, Shearlet Transform

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