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A Fast Method for Despeckling in Wavelet Domain Using Laplacian Prior and Rayleigh Noise Publisher



Rabbani H1
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Authors Affiliations
  1. 1. Department of Physics and Biomedical Engineering, Isfahan University of Medical Sciences, Isfahan, Iran

Source: 5th Int. Conference on Information Technology and Applications in Biomedicine, ITAB 2008 in conjunction with 2nd Int. Symposium and Summer School on Biomedical and Health Engineering, IS3BHE 2008 Published:2008


Abstract

In this paper we introduce a new speckle noise reduction algorithm using new probability density functions (pdfs) for log-transformed data in wavelet domain, i.e., Laplacian pdf for clean data and Rayleigh pdf for noise. The maximum a posteriori (MAP) estimator is employed to obtain the clean data from noisy observation. The Laplacian pdf is able to model the heavy-tailed nature of wavelet coefficients and since our new despeckling algorithm is implemented locally, we can model the most important dependency between wavelet coefficients. We examine our fast algorithm for both real and artificial speckle noise and achieve satisfactory performance both visually and qualitatively. © 2008 IEEE.
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