Shearlet-based adaptive MMSE estimator for image denoising

Author

Tian, Wei ; Cao, Hanwen ; Deng, Chengzhi

Author_Institution

Dept. of Inf. Eng., Nanchang Inst. of Technol., Nanchang, China

Volume

2

fYear

2010

fDate

29-31 Oct. 2010

Firstpage

689

Lastpage

692

Abstract

An adaptive Bayesian estimator for image denoising in shearlet domain is presented, where the Bessel K Form (BKF) densities are used as the prior model of shearlet coefficients of images. The BKF densities are shown to fit very well to the observed noise-free histograms. Under this prior, a Bayesian sheartlet estimator is derived by using the minimum mean square error (MMSE) rule. Finally, a simulation is carried out to show the effectiveness of the new estimator. Experimental results show the proposed method can effectively reduce noise and remain edges, obtain better visual effect and higher PSNR.

Keywords

Bayes methods; Bessel functions; adaptive estimation; image denoising; least mean squares methods; wavelet transforms; Bayesian sheartlet estimator; Bessel K Form densities; adaptive Bayesian estimator; adaptive MMSE estimator; image denoising; minimum mean square error rule; noise-free histograms; shearlet coefficients; visual effect; Bayesian methods; Noise reduction; Bayesian estimator; Bessel K Form densities; Image denoising; Shearlet transform;

fLanguage

English

Publisher

ieee

Conference_Titel

Intelligent Computing and Intelligent Systems (ICIS), 2010 IEEE International Conference on

Conference_Location

Xiamen

Print_ISBN

978-1-4244-6582-8

Type

conf

DOI

10.1109/ICICISYS.2010.5658462

Filename

5658462