Image Denoising Based on a Mixture of Laplace Distributions with Local Parameters in Complex Wavelet Domain

2006 ◽  
Author(s):  
Hossein Rabbani ◽  
Mansur Vafadust ◽  
Saeed Gazor
Keyword(s):  
Image Denoising ◽  
Wavelet Domain ◽  
Complex Wavelet ◽  
Local Parameters

scholarly journals Optical Coherence Tomography Noise Reduction Using Anisotropic Local Bivariate Gaussian Mixture Prior in 3D Complex Wavelet Domain

2013 ◽  
Vol 2013 ◽  
pp. 1-23 ◽  
Author(s):  
Hossein Rabbani ◽  
Milan Sonka ◽  
Michael D. Abramoff
Keyword(s):  
A Priori ◽  
Gaussian Mixture ◽  
Parameters Estimation ◽  
Wavelet Domain ◽  
Free Data ◽  
Mixture Prior ◽  
Mmse Estimator ◽  
Complex Wavelet ◽  
Heavy Tailed ◽  
Local Parameters

In this paper, MMSE estimator is employed for noise-free 3D OCT data recovery in 3D complex wavelet domain. Since the proposed distribution for noise-free data plays a key role in the performance of MMSE estimator, a priori distribution for the pdf of noise-free 3D complex wavelet coefficients is proposed which is able to model the main statistical properties of wavelets. We model the coefficients with a mixture of two bivariate Gaussian pdfs with local parameters which are able to capture the heavy-tailed property and inter- and intrascale dependencies of coefficients. In addition, based on the special structure of OCT images, we use an anisotropic windowing procedure for local parameters estimation that results in visual quality improvement. On this base, several OCT despeckling algorithms are obtained based on using Gaussian/two-sided Rayleigh noise distribution and homomorphic/nonhomomorphic model. In order to evaluate the performance of the proposed algorithm, we use 156 selected ROIs from 650 × 512 × 128 OCT dataset in the presence of wet AMD pathology. Our simulations show that the best MMSE estimator using local bivariate mixture prior is for the nonhomomorphic model in the presence of Gaussian noise which results in an improvement of 7.8 ± 1.7 in CNR.

Sign in / Sign up

Export Citation Format

Share Document