DocumentCode :
3256669
Title :
Gaussian mixture Markov random field for image denoising and reconstruction
Author :
Ruoqiao Zhang ; Bouman, Charles A. ; Thibault, Jean-Baptiste ; Sauer, Ken D.
Author_Institution :
Sch. of Electr. & Comput. Eng., Purdue Univ., West Lafayette, IN, USA
fYear :
2013
fDate :
3-5 Dec. 2013
Firstpage :
1089
Lastpage :
1092
Abstract :
Markov random fields (MRFs) have been widely used as prior models in various inverse problems such as tomographic reconstruction. While MRFs provide a simple and often effective way to model the spatial dependencies in images, they suffer from the fact that parameter estimate is difficult. In practice, this means that MRFs typically have very simple structure that cannot completely capture the subtle characteristics of complex images. In this paper, we present a novel Gaussian mixture Markov random field model (GM-MRF) that can be used as a very expressive prior model for inverse problems such as denoising and reconstruction. This method forms a global image model by merging together individual Gaussian-mixture models for image patches. Moreover, we present a novel analytical framework for computing MAP estimates with the GM-MRF prior model through the construction of exact surrogate functions that result in a sequence of quadratic optimizations. We demonstrate the value of the approach with some simple applications to denoising of dual-energy CT images.
Keywords :
Gaussian processes; Markov processes; image denoising; image reconstruction; maximum likelihood estimation; mixture models; optimisation; GM-MRF; Gaussian mixture; MAP estimates; Markov random field; global image model; image denoising; image patches; image reconstruction; inverse problem; quadratic optimization; tomographic reconstruction; Computational modeling; Computed tomography; Image reconstruction; Materials; Noise reduction; Optimization; PSNR; Gaussian mixture; Markov random fields; image model; patch-based methods; prior model;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Global Conference on Signal and Information Processing (GlobalSIP), 2013 IEEE
Conference_Location :
Austin, TX
Type :
conf
DOI :
10.1109/GlobalSIP.2013.6737083
Filename :
6737083
Link To Document :
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