A Closed form solution of MMSE using Multivariate Radial-Exponential Priors for Wavelet-Based image denoising
The Performance of various estimators,such as minimum mean square error (MMSE) is strongly dependent on correctness of the proposed model for original data distribution.Therefore,the selection of a proper model for distribution of wavelet coefficients is important in wavelet based image denoising.This paper presents a new image denoising algorithm based on the modeling of wavelet coefficients in each Subband with multivariate radial exponential probability density function (pdfs) with local variance.Generally these multivariate extensions do not result in a closed form expression,and the solution requires numerical solutions as in 1.However,we drive a closed form MMSE shrinkage functions for a radial exponential random vector in Gaussian noise.Experimental results show that for images of structural textures,for example ‘Barbaraand texture image,our proposed method,MMSE_TriShrink _Radial,have better PSNR than MMSE_TriShrink_Laplace 2,CauchyShrinkL 3 and BayeShrink 6.
MMSE (Minimum Mean Square Error)estimation Radial Exponential random vector Wavelet Transform
P.Kittisuwan W.Asdornwised
Digital Signal Processing Research Laboratory,Department of Electrical Engineering,Faculty of Engineering,Chulalongkorn University,Bangkok,10330,Thailand
国际会议
9th International Conference on Signal Processing(第九届国际信号处理学术会议)(ICSP08)
北京
英文
2008-10-26（万方平台首次上网日期，不代表论文的发表时间）