International Journal of Information Engineering and Electronic Business (IJIEEB)
IJIEEB Vol. 3, No. 3, 8 Jun. 2011
Cover page and Table of Contents: PDF (size: 401KB)
Image Restoration Algorithm Research on Local Motion-blur
Full Text (PDF, 401KB), PP.23-29
Views: 0 Downloads: 0
Index Terms
Local motion-blur, complex background, physical method, image restoration
Abstract
In this paper, we aim at the restoration of local motion-blur. On the base of construction of basic model of local motion-blur, the formation mechanism of local motion-blur is analyzed, and a new restoration algorithm aimed at local motion-blur in a complex background is proposed. In the algorithm, the problem of restoration of blurred image with complex background is simplified. First, the blurred part is extracted from the complex background, and then it is pasted onto a bottom with monochromatic background. After restoration in the monochromatic background, the restored part is pasted back to the original complex background. All the operations can be completed in spatial domain. Because the restoration of blur image with monochromatic background is easier, so the algorithm proposed in this paper is simple, fast and effectual. It is an effective method of blur image restoration.
Cite This Paper
Yan Chen, Jin Hua, "Image Restoration Algorithm Research on Local Motion-blur", International Journal of Information Engineering and Electronic Business(IJIEEB), vol.3, no.3, pp.23-29, 2011. DOI:10.5815/ijieeb.2011.03.04
Reference
[1]M. Y. Cao. Digital Image Processing. BeiJing: Beijing University Press, 2007.(in Chinese)
[2]Nayar S K, Ben-Ezra M. Motion-based motion deblurring [J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2004, 26 (6) : 689～698.
[3]Canon Inc [EB/OL]. http://www.canon.com/camera-museum/tech/room/tebure.html, 2011.
[4]Liu X, El Gamal A. Simultaneous image formation and motion blur restoration via multip le cap ture [A]. In: Proceedings of IEEE Conference on Acoustics, Speech, and Signal Processing[C] , Salt Lake City, UT, USA, 2001, 3: 1841～1844.
[5]Gonzalez Rafael C, Woods Richard E. Digital image processing[M]. Second Edition, NewYork, USA: Addison-Wesley Pub Co, 2002:336-370.
[6]Likhterov, Boris, Kopeika, Norman S. Motion-blurred image restoration using modified inverse all-pole filters.Proceedings of SPIE-The International Society for Optical Engineering, 2002,4790:56-62.
[7]L. Gao，S. Y. Yang，H. Q. Li. New Unsupervised image segmentation via marker-based watershed. Journal of Image and Graphics. 2007, 6(12):1025-1032. (in Chinese)
[8]Lim H, Tan K C, Tan B T G. Edge Errors in Inverse and Wiener Filter Restorations of Motion-blurred Images and Their Windowing Treatment. CVGIP, 1991, 53: 186-195.
[9]B.R.Frieden. Restoring with maximum likelihood and maxi-mum entropy[J]. Opt.Soc.Am, 1972，62:511-518.
[10]E.Panti，J.L.Starck. Deconvolution of astronomical images using the multiscale maximum entropy method. Astron. As-trophys. Suppl. Ser. 1996, (9): 515-585.
[11]RICHARDSON W H. Bayesian-based iterative method of image restoration. J. Opt . Soc. Amer . ,1974, 62( 1) : 55-59.
[12]LUCY L B. An iter ative technique for t he recti-fication of observed distributions. Astronom. J. ,1974, 79( 6) : 745-754.
[13]Ayers G R, Dainty J G. Iterative Blind Deconvolution Method and its Application[J ] . Opt Lett , 1988 , 13 (7) :547 - 549.
[14]Schulte S, Morillas S, Gregori V, etc. A new fuzzycolor correlated impulse noise reduction method. IEEE Trans Image Process. 2007, 16(10):2565-2575.
[15]Analog Device Inc. Blackfin Processor Hardware Reference Revision 3.0[S]. 2004.
[16]D. Hearn. Computer Graphics. Beijing: Electronic Industry Press. 2002,5. (in Chinese)
[17]Z. Wang, A. C. Bovik. A universal image quality index. IEEE Singal Processing Letters. 2002,3:81-84.
[18]L. D. Cai. Images blur caused by uniform linear motion and its removal using traveling wave equation and Hough transform. Proceedings of SPIE. The International Society for Optical Engineering. 1997:181-202.
[19]L. Chen, K. H. Yap. Efficient discrete spatial techniques for blur support identification in blind image deconvolution [J]. Signal Processing, IEEE Transactions on. 2006, 54(4):1557-1562.
[20]M. E. Moghaddam, M. Jamzad. Motion blur identification in noisy images using mathematical models and statistical measures [J]. Pattern Recognition (S0031-3203) ，2007，40 (7) ：1946-1957.
[21]R. T. Liu, Z. R. Li, J. Y. Jia. Image partial blur detection and classification [C]// IEEE Conference on Computer Vision and Pattern Recognition, Anchorage, Alaska, USA, June 23-28, 2008：954-961.
[22]J. Hua, Y. Chen, S. Y. Huang. Research on local motion-blurred image restoration algorithm based on physical methods. The 2nd International Conference on Information Engineering and Computer Science, ICIECS 2010, 2010.
[23]X. Meng, Y. P. Zhang. Study and analysis of motion-blurred image restoration algorithm. Computer technology and development. 2007,17(8).
[24]J. H.Wang, W. J. Liu, and L. D. Lin. Histogram-Based fuzzy filter for image restoration. IEEE Trans. Syst., Man, Cybern. B, Cybern., 2002,32(4)230–238.
[25]K. Arakawa. Fuzzy rule-based image processing with optimization Springer. E.E. Kerre, M. Nachtegael (Eds.). FuzzyTechniques in Image Processing, New York, 2000:222–247.
[26]Yitzhaky Y, Lantzman A, Mor I ,et al. Evaluation of t he PSF f rom Motion Blurred Images [ C]∥Proc of SPIE’05, 2005, 822-823.
[27]C.S. Lee, Y.H. Kuo. Adaptive fuzzy filter and its application to image enhancement. E.E. Kerre, M. Nachtegael (Eds.). Fuzzy Techniques in Image Processing Springer. New York, 2000:172–193.