JACIII Vol.20 No.1 pp. 155-162
doi: 10.20965/jaciii.2016.p0155


Research on Cross-Correlative Blur Length Estimation Algorithm in Motion Blur Image

Li Dongming*1, Su Zhengbo*2, Su Wei*3,†, and Zhang Lijuan*4

*1School of Information Technology, Jilin Agriculture University
No.2888, XinCheng Street, Changchun City, Jilin 130118, China

*2School of Computer Science and Technology, Harbin Institute of Technology
92 West Dazhi Street, Harbin, Heilongjiang 150001, China

*3Informatization Center, Changchun University of Science and Technology
No.7089, Rd. Weixing, Changchun, Jilin 130022, China

*4College of Computer Science and Engineering, Changchun University of Technology
No.2055, YanAn Street, Changchun City, Jilin 130012, China

Corresponding author

November 10, 2015
December 10, 2015
Online released:
January 19, 2016
January 20, 2016
cross correlation, motion blur image, blur length, point spread function (PSF), radon transform
This paper proposes a motion blur length estimation method that is applied to motion blur image restoration. This method applies a cross-correlation algorithm to multi-frame motion-degraded images. In order to find the motion blur parameters, the Radon transform method is used to estimate the motion blur angle. We extract the gray value of pixels around the blur center, calculate the correlation for obtaining motion blur length, and use the Lucy-Richardson iterative algorithm to restore the degraded image. Experiment results show that this method can accurately estimate blur parameters, reduce noise, and obtain better restoration results. The method achieves good results on artificially blurred images and natural images (by the camera shake). The advantage of our algorithm that uses the Lucy-Richardson restoration algorithm compared with the Wiener filtering algorithm is made obvious with less computation time and better restored effects.
Cite this article as:
L. Dongming, S. Zhengbo, S. Wei, and Z. Lijuan, “Research on Cross-Correlative Blur Length Estimation Algorithm in Motion Blur Image,” J. Adv. Comput. Intell. Intell. Inform., Vol.20 No.1, pp. 155-162, 2016.
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Last updated on May. 19, 2024