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  • Research Article
  • Open Access

An Adaptively Accelerated Lucy-Richardson Method for Image Deblurring

  • 1,
  • 2 and
  • 1Email author
EURASIP Journal on Advances in Signal Processing20072008:365021

https://doi.org/10.1155/2008/365021

  • Received: 11 June 2007
  • Accepted: 3 December 2007
  • Published:

Abstract

We present an adaptively accelerated Lucy-Richardson (AALR) method for the restoration of an image from its blurred and noisy version. The conventional Lucy-Richardson (LR) method is nonlinear and therefore its convergence is very slow. We present a novel method to accelerate the existing LR method by using an exponent on the correction ratio of LR. This exponent is computed adaptively in each iteration, using first-order derivatives of the deblurred image from previous two iterations. Upon using this exponent, the AALR improves speed at the first stages and ensures stability at later stages of iteration. An expression for the estimation of the acceleration step size in AALR method is derived. The superresolution and noise amplification characteristics of the proposed method are investigated analytically. Our proposed AALR method shows better results in terms of low root mean square error (RMSE) and higher signal-to-noise ratio (SNR), in approximately 43% fewer iterations than those required for LR method. Moreover, AALR method followed by wavelet-domain denoising yields a better result than the recently published state-of-the-art methods.

Keywords

  • Information Technology
  • Root Mean Square Error
  • Deblurring
  • Quantum Information
  • Full Article

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Authors’ Affiliations

(1)
Sensor System Laboratory, Department of Mechatronics, Gwangju Institute of Science and Technology (GIST), 1 Oryong-dong, Buk-gu, Gwangju, 500 712, South Korea
(2)
Indian Institute of Information Technology Allahabad (IIITA), Deoghat Jhalwa, Allahabad, 211012, India

Copyright

© 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.

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