Motion blur adaptive identification from natural image model

Authors:
Hongwei Sun;Michel Desvignes;Yunhui Yan
Affiliations:
DIS Gipsa-lab, Grenoble Institute of Technology, France and School of Mechanical Engineering and Automation, Northeastern University, China;DIS Gipsa-lab, Grenoble Institute of Technology, France;School of Mechanical Engineering and Automation, Northeastern University, China
Venue:
ICIP'09 Proceedings of the 16th IEEE international conference on Image processing
Year:
2009

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Abstract

This paper proposes a novel approach to estimate the parameters of motion blur (orientation and extension) simultaneously from the observed image. The motion blur estimation would be used in a standard non blind deconvolution algorithm, thus yielding a blind motion deblurring scheme. Our algorithm is based on the correlation between the modified logarithm power spectrum from natural image model and the blur kernel. The local minima of the modified spectrum are closer to the horizontal line, and thus more similar to the sinc function. Compared to previous estimation algorithm, the results are more accurate in noisy images.