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

Li Dongming; Su Zhengbo; Su Wei; Zhang Lijuan

doi:10.20965/jaciii.2016.p0155

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JACIII Vol.20 No.1 pp. 155-162

doi: 10.20965/jaciii.2016.p0155

(2016)

Paper:

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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

Received:

November 10, 2015

Accepted:

December 10, 2015

Online released:

January 19, 2016

Published:

January 20, 2016

Keywords:

cross correlation, motion blur image, blur length, point spread function (PSF), radon transform

Abstract

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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References

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[2] T. F. Chan and C. K. Wong, “Total variation blind deconvolution,” IEEE Trans. Image Process, Vol.7, No.3, pp. 370-375, 1998.
[3] C. Mayntz and T. Aach, “Blur identification using a spectral inertia tensor and spectral zeros,” Proc. IEEE Int. Conf. Image Proc, pp. 885-889, 1999.
[4] M. M. Chang, A. M. Tekalp, and A. T. Erdem, “Blur identification using the bispectrum,” IEEE Trans. Acoust. Speech Signal Process, Vol.39, pp. 2323-2325, 1991.
[5] Z. Yu and Z. Xu-sheng, “An improved motion blur image restoration algorithm,” Computer Engineering and Science, Vol.31, No.10, pp. 491-500, 2009.
[6] M. R. Baham and A. K. Katsagellos, “Digital image restoration,” IEEE Signal Processing Magazine, Vol.14, No.2, pp. 24-41, 1997.
[7] M. Ebranhimi Moghaddam and M. Jamzad, “Motion blur identification in noisy images using mathematical models and statistical measures,” Pattern Recognition, Vol.40, pp. 1946-1957, 2007.
[8] H. Haogang and L. Yan-ling, “Blurred Image Restoration based on the movement of direction information measure,” Computer Engineering, Vol.36, No.2, pp. 201-202, 2010.
[9] Z. De, “Digital Image Processing (MATLAB),” Beijing: Posts & Telecom Press, pp. 176-184, 2009.
[10] G. Bonmassar and E. L. Schwartz, “Real-time restoration of images degraded by uniform motion blur in foveal active systems,” IEEE Trans. on Image Processing, Vol.8, No.12, pp. 1838-1842, 1999.
[11] H. Hanyu and Z. Tianxu, “Fast restoration approach for rotational motion blurred image based on deconvolution along the blurring paths,” Optical Engineering, Vol.42, No.12, pp. 3471-3486, 2003.
[12] Y. Yitzhaky, I. Mor, and A. Lantzman, “A direct method for restoration of motion blurred images,” J. Opt. Soc. Am. A, Vol.15, No.6, pp. 1512-1519, 1998.
[13] M. R. Banham and A. K. Katsagellos, “Digital image restoration,” IEEE Signal Processing Mag., 14, pp. 24-41, 1997.
[14] USC-SIPI Image Database, Online Image Database Available: http://sipi.usc.edu/database [Accessed May 2015]

This article is published under a Creative Commons Attribution-NoDerivatives 4.0 Internationa License.

[1] [1] M. Ben-Ezra and Nayar, “Motion deblurring using hybrid imaging,” IEEE Conf. on Computer Vision and Pattern Recognition, Madison, Winsconsin, pp. 454-460, 2003.

[2] [2] T. F. Chan and C. K. Wong, “Total variation blind deconvolution,” IEEE Trans. Image Process, Vol.7, No.3, pp. 370-375, 1998.

[3] [3] C. Mayntz and T. Aach, “Blur identification using a spectral inertia tensor and spectral zeros,” Proc. IEEE Int. Conf. Image Proc, pp. 885-889, 1999.

[4] [4] M. M. Chang, A. M. Tekalp, and A. T. Erdem, “Blur identification using the bispectrum,” IEEE Trans. Acoust. Speech Signal Process, Vol.39, pp. 2323-2325, 1991.

[5] [5] Z. Yu and Z. Xu-sheng, “An improved motion blur image restoration algorithm,” Computer Engineering and Science, Vol.31, No.10, pp. 491-500, 2009.

[6] [6] M. R. Baham and A. K. Katsagellos, “Digital image restoration,” IEEE Signal Processing Magazine, Vol.14, No.2, pp. 24-41, 1997.

[7] [7] M. Ebranhimi Moghaddam and M. Jamzad, “Motion blur identification in noisy images using mathematical models and statistical measures,” Pattern Recognition, Vol.40, pp. 1946-1957, 2007.

[8] [8] H. Haogang and L. Yan-ling, “Blurred Image Restoration based on the movement of direction information measure,” Computer Engineering, Vol.36, No.2, pp. 201-202, 2010.

[9] [9] Z. De, “Digital Image Processing (MATLAB),” Beijing: Posts & Telecom Press, pp. 176-184, 2009.

[10] [10] G. Bonmassar and E. L. Schwartz, “Real-time restoration of images degraded by uniform motion blur in foveal active systems,” IEEE Trans. on Image Processing, Vol.8, No.12, pp. 1838-1842, 1999.

[11] [11] H. Hanyu and Z. Tianxu, “Fast restoration approach for rotational motion blurred image based on deconvolution along the blurring paths,” Optical Engineering, Vol.42, No.12, pp. 3471-3486, 2003.

[12] [12] Y. Yitzhaky, I. Mor, and A. Lantzman, “A direct method for restoration of motion blurred images,” J. Opt. Soc. Am. A, Vol.15, No.6, pp. 1512-1519, 1998.

[13] [13] M. R. Banham and A. K. Katsagellos, “Digital image restoration,” IEEE Signal Processing Mag., 14, pp. 24-41, 1997.

[14] [14] USC-SIPI Image Database, Online Image Database Available: http://sipi.usc.edu/database [Accessed May 2015]

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

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