PSF Estimation and Image Restoration for Motion Blurred Images

Abstract:

Point spread function (PSF) estimation and image restoration algorithm are the hotspots In the research of motion blurred image restoration. In order to improve the efficacy of image restoration, an improved algorithm named quadric transforms (QT) method is proposed in this paper by analyzing the restoration process of motion blurred images. Firstly, Fourier transform and homomorphism transform are applied to the original motion blurred image, and then the Fourier transform and homomorphism transform are used again to the obtained spectrum image. Secondly, the motion blur direction is estimated by Radon transform. Thirdly, the motion blur length is found by differential autocorrelation operations. Finally, utilizing the estimated blur direction and blur length, the motion blurred image is restored by Wiener filtering. Experimental results show that the proposed QT method can get more accurate estimated motion blur angles than the primary transform (PT, that is, Fourier transform and homomorphism transform are used one time) method and can get better restored images under the meaning of peak signal to noise ratio (PSNR).

• Periodical: Advanced Materials Research (Volumes 562-564)
• Pages: 2124-2127
• DOI: https://doi.org/10.4028/www.scientific.net/AMR.562-564.2124
• Online since: August 2012
• Authors: Qi Shen Li, Jian Gong Chen
• Keywords: Differential Autocorrelation, Motion Blurred, Point Spread Function, Radon Transform

References

[1] H Zhang and D Y Luo. Status and development of study on blind image restoration algorithm. Journal of Image and Graphics, 2004, 9(10): 1145-1152.

[2] M Cannon. Blind deconvolution of spatially invariant image blurs with phase. IEEE Transaction on Acoustics, Speech and Signal Processing, 1976, 24(1): 58-63.

DOI: 10.1109/tassp.1976.1162770

[3] Y Yitzhaky and N S Kopeika. Identification of blur parameters from motion blurred images. Graphical Models and Image Process, 1997, 59(5): 310-320.

DOI: 10.1006/gmip.1997.0435

[4] Y F Jiang. Identification of motion blur direction from motion blurred image by spectrum analysis. Optics & Optoelectronic Technology, 2008, 6(4): 69-70.

[5] Q R Chen, Q S Lu and L Z Cheng. Identification of the motion blurred direction of motion blurred images. Journal of National University of Defense Technology, 2004, 26(1): 41-45.

[6] Y C Guo, R R Guo and C Gao. Identification of blur parameters from motion blurred images. Chinese Journal of Scientific Instrument, 2010, 31(5): 1052-1057.

[7] T Pang and X P Cheng. Parameters estimation of motion blurred images based on Radon transformtion. Science Technology and Engineering, 2010, 10(22): 5551-5554.

[8] Q Li and Y Yoshida. Parameter estimation and restoration for motion blurred images. IEICE Transaction on Fundamentals of Electronics, Communications and Computer Sciences, 1997, E80-A(8): 1430-1437.

[9] M Dobes, L Machala and T Furst. Blurred image restoration: A fast method of finding the motion length and angle. Digital Signal Processing, 2010, 20(6): 1677-1686.

DOI: 10.1016/j.dsp.2010.03.012

[10] J Oliveira. Implementation of an exact Radon transform. Technical Report, Instituto de Telecomunicacoes, Lisboa, (2006).