Local adaptive image denoising based on minimum Bayes risk in wavelet domain

Abstract

The paper briefly introduced the basic conception of Generalized Gaussian Distribution(GGD) and studied the distributional property of wavelet coefficients,then analyzed the principle of BayesShrink and pointed out the exiting shortcomings.A local adaptive wavelet denoising algorithm was proposed based on the redundant wavelet transform and the relativity among wavelet coefficients in the subband.The new method selected a proper neighboring window by centering the current coefficient within it,and estimated the corresponding ideal standard deviation and threshold for the centered coefficient,and then made shrinkage on it by soft thresholding.The experimental results show the new method effectively filters the noise,reserves more texture and detail of images and gets higher Peak Signal-to-Noise Ratio(PSNR) value and better visual expression.