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Reseach Article

A Non-uniform Motion Blur Parameter Identification and Restoration using Frequency and Cepstral Domain

Published on March 2012 by Ashwini M. Deshpande, Suprava Patnaik
International Conference and Workshop on Emerging Trends in Technology
Foundation of Computer Science USA
ICWET2012 - Number 3
March 2012
Authors: Ashwini M. Deshpande, Suprava Patnaik
a600b90d-755c-46d2-8b2a-47a7f7bf8021

Ashwini M. Deshpande, Suprava Patnaik . A Non-uniform Motion Blur Parameter Identification and Restoration using Frequency and Cepstral Domain. International Conference and Workshop on Emerging Trends in Technology. ICWET2012, 3 (March 2012), 11-17.

@article{
author = { Ashwini M. Deshpande, Suprava Patnaik },
title = { A Non-uniform Motion Blur Parameter Identification and Restoration using Frequency and Cepstral Domain },
journal = { International Conference and Workshop on Emerging Trends in Technology },
issue_date = { March 2012 },
volume = { ICWET2012 },
number = { 3 },
month = { March },
year = { 2012 },
issn = 0975-8887,
pages = { 11-17 },
numpages = 7,
url = { /proceedings/icwet2012/number3/5328-1019/ },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Proceeding Article
%1 International Conference and Workshop on Emerging Trends in Technology
%A Ashwini M. Deshpande
%A Suprava Patnaik
%T A Non-uniform Motion Blur Parameter Identification and Restoration using Frequency and Cepstral Domain
%J International Conference and Workshop on Emerging Trends in Technology
%@ 0975-8887
%V ICWET2012
%N 3
%P 11-17
%D 2012
%I International Journal of Computer Applications
Abstract

A near accurate method for extracting blur parameters from a non-uniformly motion blurred images; in a blind image deconvolution scheme is proposed. In case of a non-uniform motion blur, we should be able to extract both the blur parameters and the combination of their extent fairly accurate, in order to improve the quality of the restored image. Initially, the parameters of the motion blur point spread function (PSF) of the observed blurry image are estimated. The blur parameters, which consist of two different directions and lengths of motion, can be extracted from the spectral and cepstral domain responses respectively, of that of the blurred image. Thereafter the morphological filtering is employed to enhance the precision of the directions and the lengths identification. Further, the estimated point spread functions (PSFs) of the motion blur are used to model the degradation function. A parametric Wiener filter performs deconvolution using the estimated PSF parameters and helps restoring these non-uniformly motion blurred images. The experimental results show that the performance of the algorithm proposed in this paper has higher PSF parameter estimation accuracy.

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Index Terms

Computer Science
Information Sciences

Keywords

Non-uniform Motion blur Spatial-variance PSF Spectrum Cepstrum Blind deconvolution