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Moving object’s image deploring algorithm using image sequence

Keywords:

A.G. Tashlinskii – Dr. Sci. (Eng.), professor, head of Radio engineering department, Ulyanovsk State Technical University. E-mail: tag@ulstu.ru
P.V. Smirnov – Post-graduate student, Ulyanovsk State Technical University. E-mail: rtcis@mail.ru


Images of moving object are often distorted due to its high speed. The most significant type of distortion is blur. Formulation of image restoration problem is the following. Video sequence from a camera contains images of a moving object which are noisy and blurred. The camera may also move. Using video sequence we need to find moving object, deblur its image, estimate its motion pattern, and filter additive noise. The paper describes main stages of the task: moving object region detection using frame sequence, blurring parameters estimation, object image restoration, estimation of location parameters of moving object (or its defined part), restored image registration to increase signal-to-noise ratio. New algorithm for motion region detection based on stochastic gradient procedure is proposed. The algorithm is resistant to noise and allows eliminating influence of global interframe geometric deformations and motion of small objects. The algorithm estimates blurring parameters using relation between them and cepstrum minimum location as well as property of cepstrum addition. We chose iterative algorithm for image restoration due to its ability to reduce noise and edge effects. Adaptive stochastic gradient procedures without identification are used to estimate object location parameters. The algorithm does not require a priori information of blurring parameters and can be fully automatized. Its main advantage is high processing speed due to joint use of detection, registration and restoration recurrent procedures.
References:

  1. Bogush R.P., Samoshhenkov G.A. Analiz algoritmov obrabotki dinamicheskix izobrazhenij v prilozhenii k zadache segmentaczii dvizheniya na slozhnom fone / Materialy' Mezhdunarodnoj zaochnoj NTK «Aktual'ny'e voprosy' sovremennoj informatiki-2011» 1-15 aprelya 2011. Kolomna: MGOSGI. 2011. T. 2. S. 13-16.
  2. Weng M., Huang G., Da X. A new interframe difference algorithm for moving target detection // Image and Signal Processing (CISP). 2010. V. 1. P. 285-289.
  3. Elhabian Sh.Y., El-Sayed Kh.M., Ahmed S.H. Moving Object Detection in Spatial Domain using Background Removal Techniques // Recent Patents on Computer Science. 2008. V. 1. P. 32-54.
  4. Karasulu B., Korukoglu S. Performance Evaluation Software: Moving Object Detection and Tracking in Videos // SpringerBriefs in Computer Science. 2013. P. 76.
  5. Skripkina A.A. Obzor metodov obnaruzheniya dvizhushhegosya ob''ekta po videoizobrazheniyam // Perspektivy' razvitiya informaczionny'x texnologij. 2011. № 3-1. S. 126-129.
  6. Grishin S.V., Vatolin D.S., Lukin A.S. i dr. Obzor blochny'x metodov oczenki dvizheniya v czifrovy'x video signalax // Tematicheskij sbornik «Programmny'e sistemy' i instrumenty'». 2008. T. 9. S. 50-62.
  7. Bogush R.P., Ly'senko V.Ju., Samoshhenkov G.A. Kombinirovanie blochny'x algoritmov vy'chisleniya opticheskogo potoka dlya obnaruzheniya i soprovozhdeniya dvizhushhixsya ob''ektov na videoposledovatel'nostyax // Vestnik Poloczkogo gosudarstvennogo universitetata. Ser. Fundamental'ny'e nauki. 2011. № 4. S. 2-7.
  8. Jing X., Chau L.-P. An Efficient Three-Step Search Algorithm for Block Motion Estimation // IEEE Trans. on Multimedia. 2004. V. 6. № 3. P. 435-438.
  9. Pons D., Forsajt Zh. Komp'yuternoe zrenie. Sovremenny'j podxod. M.: Vil'yams. 2004. 926 s.
  10. Tashlinskij A.G. Oczenivanie parametrov prostranstvenny'x deformaczij posledovatel'nostej. Ul'yanovsk: Izdatel'stvo UlGTU. 2000. 132 s.
  11. Tashlinskij A.G. Psevdogradientnoe oczenivanie prostranstvenny'x deformaczij posledovatel'nosti izobrazhenij // Naukoemkie texnologii. 2002. T. 3. S. 32-43.
  12. Jagola A.G., Koshev N.A. Vosstanovlenie smazanny'x i defokusirovanny'x czvetny'x izobrazhenij // Vy'chislitel'ny'e metody' i programmirovanie. 2008. № 2. S. 207-212.
  13. Machixin A.S. Avtomaticheskoe vosstanovlenie izobrazhenij, iskazhenny'x pryamolinejny'm smazom // Izv. vuzov. Ser. Priborostroenie. 2008. T. 51. № 1. S. 59-64.
  14. Shiqian W., Zhongkang L. Blind Image Blur Identification in Cepstrum Domain // Computer Communications and Networks. 2007. P. 1166-1171.
  15. Zitova B., Flusser J. Image registration methods: a survey // Image Vision Comput. 2003. V. 21. P. 977-1000.
  16. Tashlinskii A.G., Safina G.L., Voronov S.V. Pseudogradient optimization of objective function in estimation of geometric interframe image deformations // Pattern Recognition and Image Analysis. 2012. V. 22. № 2. P. 386-392.
  17. Tashlinskii A.G., Smirnov P.V., Zhukov S.S. Analysis of methods of estimating objective function gradient during recurrent measurements of image parameters // Pattern recognition and image analysis. 2012. V. 22. № 3. P. 399-405.
  18. Vasil'ev K.K., Krasheninnikov V.R., Tashlinskij A.G. Statisticheskij analiz posledovatel'nostej mnogomerny'x izobrazhenij // Naukoemkie texnologii. 2013. T. 14. № 5. S. 5-11.

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