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Activity Number: 552
Type: Topic Contributed
Date/Time: Wednesday, August 6, 2014 : 2:00 PM to 3:50 PM
Sponsor: Section on Bayesian Statistical Science
Abstract #311079 View Presentation
Title: Bayesian Multiscale Smoothing of Gaussian Noised Images
Author(s): Meng Li*+ and Subhashis Ghosal
Companies: and North Carolina State University
Keywords: Chinese Restaurant Process ; MCMC-free computation ; 3-dimensional image
Abstract:

We propose a multiscale model for Gaussian noised images under a Bayesian framework for both 2-dimensional (2D) and 3-dimensional (3D) images. We use a Chinese restaurant process prior to randomly generate ties among intensity values of pixels in the neighboring image. The resulting Bayesian estimator enjoys some desirable asymptotic properties for identifying precise structures in the image, and is completely data-driven. A conditional conjugacy property allows analytical computation of the posterior distribution without involving Markov chain Monte Carlo (MCMC) methods, making the method computationally efficient. Simulations based on Shepp-Logan phantom and Lena test images confirm that our smoothing method is comparable with the best available methods for light noises and outperforms them for heavier noises both visually and numerically. The proposed method is easily extendedfor 3D images in the paper. A simulation study shows that our method is numerically better than most existing denoising approaches for 3D images. Matlab toolboxes are made available online (both 2D and 3D) to implement the proposed method and reproduce the numerical results.


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