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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Series: | EURASIP Journal on Advances in Signal Processing |
Online Access: | http://dx.doi.org/10.1155/2008/674038 |
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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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