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Optical Engineering

Motion blur estimation based on multitarget matching model
Author(s): Victor Karnaukhov; Mikhail Mozerov
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Paper Abstract

We propose a new method to estimate motion blur parameters based on the autocorrelation function of a blurred image. This blurred image is considered as a superposition of M shifted images identical to the original nonblurred image. In this case, convolution of the blurred image with itself can be considered as M2 pairwise convolutions, which contribute to the resultant autocorrelation function producing a distinguishable line corresponding to the estimated motion blur angle. The proposed method demonstrates the comparable accuracy of the motion blur angle estimation in comparison with state-of-the-art methods. Our method possesses lower computational complexity than popular accurate methods based on Radon transform. The proposed model also allows to accurately estimate motion blur length. Our results of length estimation, in general, outperform the accuracy of the methods based on Radon transform.

Paper Details

Date Published: 19 October 2016
PDF: 4 pages
Opt. Eng. 55(10) 100502 doi: 10.1117/1.OE.55.10.100502
Published in: Optical Engineering Volume 55, Issue 10
Show Author Affiliations
Victor Karnaukhov, Institute for Information Transmission Problems (Russian Federation)
Mikhail Mozerov, Institute for Information Transmission Problems (Russian Federation)

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