Local Adaptive Image Denoising Based on Bayesian Estimation in Shearlet Domain

Based on the combination of Bayesian estimation and Shearlet transform,an algorithm for image denoising is proposed.In order to get Shearlet coefficients in all scales and directions,image with additive white Gaussian noise is processed by Shearlet transform. With the dependencies of the Shearlet coefficients,the new method select a proper neighboring window by centering the current coefficient within it,and conclude the MAP expression and sub-band thresholds under Bayesian maximum posterior probability criteria when Shearlet coefficients is thought to have Laplace prior distribution,then made shrinkage on it by soft threshold.Inverse Shearlet transform is performed to the processed coefficients and get the denoised image.The experimental results demonstrate that compared with traditional wavelet domain denoising algorithms,the proposed method not only improves peak ratio of signal to noise but also have a remarkable visual effects.