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  • Research Article
  • Open Access

An Adaptively Accelerated Bayesian Deblurring Method with Entropy Prior

  • 1,
  • 2 and
  • 1Email author
EURASIP Journal on Advances in Signal Processing20082008:674038

  • Received: 28 August 2007
  • Accepted: 4 April 2008
  • Published:


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.


  • Entropy
  • Deblurring
  • Analytical Analysis
  • Fundamental Idea
  • Statistical Concept

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Authors’ Affiliations

Sensor System Laboratory, Department of Mechatronics, Gwangju Institute of Science and Technology (GIST), 1 Oryong-dong, Buk-gu, Gwangju, 500 712, South Korea
Indian Institute of Information Technology, Allahabad, 211-012, India


© Manoj Kumar Singh et al. 2008

This article is published under license to BioMed Central Ltd. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.