Semi-Blind Sparse Image Reconstruction With Application to MRFM

被引:10
作者
Park, Se Un [1 ]
Dobigeon, Nicolas [2 ]
Hero, Alfred O. [1 ]
机构
[1] Univ Michigan, Dept Elect Engn & Comp Sci, Ann Arbor, MI 48109 USA
[2] Univ Toulouse, Ecole Natl Super Elect Electrotech Informat Hydra, F-31071 Toulouse, France
关键词
Bayesian inference; magnetic resonance force microscopy (MRFM) experiment; Markov chain Monte Carlo (MCMC) methods; semi-blind (myopic) sparse deconvolution; DECONVOLUTION; RESTORATION;
D O I
10.1109/TIP.2012.2199505
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
We propose a solution to the image deconvolution problem where the convolution kernel or point spread function (PSF) is assumed to be only partially known. Small perturbations generated from the model are exploited to produce a few principal components explaining the PSF uncertainty in a high-dimensional space. Unlike recent developments on blind deconvolution of natural images, we assume the image is sparse in the pixel basis, a natural sparsity arising in magnetic resonance force microscopy (MRFM). Our approach adopts a Bayesian Metropolis-within-Gibbs sampling framework. The performance of our Bayesian semi-blind algorithm for sparse images is superior to previously proposed semi-blind algorithms such as the alternating minimization algorithm and blind algorithms developed for natural images. We illustrate our myopic algorithm on real MRFM tobacco virus data.
引用
收藏
页码:3838 / 3849
页数:12
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