Evaluating Separable, Stationary, and Dual-Tree Wavelets for Despeckling Based on Baysian and Bishrink Thresholding
Journal Title: روش های هوشمند در صنعت برق - Year 2018, Vol 9, Issue 36
Abstract
The presence of speckle as multiplicative noise in ultrasound and radar images defects the image perception. Therefore, it is necessary to reduce the speckle before processing like segmentation, edge detecting, and target navigation. In general, denoising is performed either in spatial or transform domain where in this paper, we focused on transform domain as well. Bayesian method and BiShrink approach which is the two-variable Bayesian, are addressed in the domains of separable, stationary, and Dual-tree wavelets for speckle noise reduction by thresholding. Based on simulation results, the Dual-tree wavelet is appropriate because of being separate the real and imaginary parts. In addition, the BiShrink method is more efficient than the Bayesian. To compare the performance of different methods, the standard Lena and Barabra test images and a real SAR image are used, MSE, PSNR, SSIM, ENL, and NV are computed as quantitative criteria. Also, in order to evaluate the coefficients sparsity, the histograms are shown and the average standarad devition values for all subbands are obtained.
Authors and Affiliations
Niku Farhangi, Sedigheh Ghofrani
Keywords
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