An Adaptively Accelerated Bayesian Deblurring Method with Entropy Prior

The development of an efficient adaptively accelerated iterative deblurring algorithm based on Bayesian statistical concept has been reported. Entropy of an image has been used as a “prior†distribution and instead of additive form, used in conventional acceleration methods an exponent form...

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Main Authors: Yong-Hoon Kim, Uma Shanker Tiwary, ManojKumar Singh
Format: Article
Language:English
Published: SpringerOpen 2008-05-01
Series:EURASIP Journal on Advances in Signal Processing
Online Access:http://dx.doi.org/10.1155/2008/674038
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spelling doaj-e81f002d74e74174ad374e56b0ce775b2020-11-24T21:10:30ZengSpringerOpenEURASIP Journal on Advances in Signal Processing1687-61721687-61802008-05-01200810.1155/2008/674038An Adaptively Accelerated Bayesian Deblurring Method with Entropy PriorYong-Hoon KimUma Shanker TiwaryManojKumar SinghThe development of an efficient adaptively accelerated iterative deblurring algorithm based on Bayesian statistical concept has been reported. Entropy of an image has been used as a “prior†distribution and instead of additive form, used in conventional acceleration methods an exponent form of relaxation constant has been used for acceleration. Thus the proposed method is called hereafter as adaptively accelerated maximum a posteriori with entropy prior (AAMAPE). Based on empirical observations in different experiments, the exponent is computed adaptively using first-order derivatives of the deblurred image from previous two iterations. This exponent improves speed of the AAMAPE method in early stages and ensures stability at later stages of iteration. In AAMAPE method, we also consider the constraint of the nonnegativity and flux conservation. The paper discusses the fundamental idea of the Bayesian image deblurring with the use of entropy as prior, and the analytical analysis of superresolution and the noise amplification characteristics of the proposed method. The experimental results show that the proposed AAMAPE method gives lower RMSE and higher SNR in 44% lesser iterations as compared to nonaccelerated maximum a posteriori with entropy prior (MAPE) method. Moreover, AAMAPE followed by wavelet wiener filtering gives better result than the state-of-the-art methods.http://dx.doi.org/10.1155/2008/674038
collection DOAJ
language English
format Article
sources DOAJ
author Yong-Hoon Kim
Uma Shanker Tiwary
ManojKumar Singh
spellingShingle Yong-Hoon Kim
Uma Shanker Tiwary
ManojKumar Singh
An Adaptively Accelerated Bayesian Deblurring Method with Entropy Prior
EURASIP Journal on Advances in Signal Processing
author_facet Yong-Hoon Kim
Uma Shanker Tiwary
ManojKumar Singh
author_sort Yong-Hoon Kim
title An Adaptively Accelerated Bayesian Deblurring Method with Entropy Prior
title_short An Adaptively Accelerated Bayesian Deblurring Method with Entropy Prior
title_full An Adaptively Accelerated Bayesian Deblurring Method with Entropy Prior
title_fullStr An Adaptively Accelerated Bayesian Deblurring Method with Entropy Prior
title_full_unstemmed An Adaptively Accelerated Bayesian Deblurring Method with Entropy Prior
title_sort adaptively accelerated bayesian deblurring method with entropy prior
publisher SpringerOpen
series EURASIP Journal on Advances in Signal Processing
issn 1687-6172
1687-6180
publishDate 2008-05-01
description The development of an efficient adaptively accelerated iterative deblurring algorithm based on Bayesian statistical concept has been reported. Entropy of an image has been used as a “prior†distribution and instead of additive form, used in conventional acceleration methods an exponent form of relaxation constant has been used for acceleration. Thus the proposed method is called hereafter as adaptively accelerated maximum a posteriori with entropy prior (AAMAPE). Based on empirical observations in different experiments, the exponent is computed adaptively using first-order derivatives of the deblurred image from previous two iterations. This exponent improves speed of the AAMAPE method in early stages and ensures stability at later stages of iteration. In AAMAPE method, we also consider the constraint of the nonnegativity and flux conservation. The paper discusses the fundamental idea of the Bayesian image deblurring with the use of entropy as prior, and the analytical analysis of superresolution and the noise amplification characteristics of the proposed method. The experimental results show that the proposed AAMAPE method gives lower RMSE and higher SNR in 44% lesser iterations as compared to nonaccelerated maximum a posteriori with entropy prior (MAPE) method. Moreover, AAMAPE followed by wavelet wiener filtering gives better result than the state-of-the-art methods.
url http://dx.doi.org/10.1155/2008/674038
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