DocumentCode :
3489539
Title :
Motion blur adaptive identification from natural image model
Author :
Sun Hongwei ; Desvignes, Michel ; Yan Yunhui
Author_Institution :
DIS Gipsa-Lab., Grenoble Inst. of Technol., Grenoble, France
fYear :
2009
fDate :
7-10 Nov. 2009
Firstpage :
137
Lastpage :
140
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.
Keywords :
deconvolution; image motion analysis; blind motion deblurring scheme; blur kernel; deconvolution algorithm; motion blur adaptive identification; natural image model; Cameras; Cepstrum; Image restoration; Kernel; Motion estimation; Noise level; Nonlinear filters; Parameter estimation; Signal to noise ratio; Wiener filter; Blur identification; Correlation; Motion blur;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Image Processing (ICIP), 2009 16th IEEE International Conference on
Conference_Location :
Cairo
ISSN :
1522-4880
Print_ISBN :
978-1-4244-5653-6
Electronic_ISBN :
1522-4880
Type :
conf
DOI :
10.1109/ICIP.2009.5414160
Filename :
5414160
Link To Document :
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