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Convergence analysis of a quadratic upper bounded TV regularizer based blind deconvolution

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Title Convergence analysis of a quadratic upper bounded TV regularizer based blind deconvolution
 
Creator RENU, MR
CHAUDHURI, S
VELMURUGAN, R
 
Subject IMAGE-RESTORATION
VARIATIONAL APPROACH
MINIMIZATION
ALGORITHM
Blind deconvolution
Total variation
Majorize-minimize
Alternate minimization
Convergence analysis
 
Description We provide a novel Fourier domain convergence analysis for blind deconvolution using the quadratic upper-bounded total variation (TV) as the regularizer. Though quadratic upper-bounded TV leads to a linear system in each step of the alternate minimization (AM) algorithm used, it is shift-variant, which makes Fourier domain analysis impossible. So we use an approximation which makes the system shift invariant at each iteration. The resultant points of convergence are better - in the sense of reflecting the data - than those obtained using a quadratic regularizer. We analyze the error due to the approximation used to make the system shift invariant. This analysis provides an insight into how TV regularization works and why it is better than the quadratic smoothness regularizer. (C) 2014 Elsevier B.V. All rights reserved.
 
Publisher ELSEVIER SCIENCE BV
 
Date 2016-01-15T05:08:30Z
2016-01-15T05:08:30Z
2015
 
Type Article
 
Identifier SIGNAL PROCESSING, 106,174-183
0165-1684
1879-2677
http://dx.doi.org/10.1016/j.sigpro.2014.06.029
http://dspace.library.iitb.ac.in/jspui/handle/100/17836
 
Language en