OSA's Digital Library

Applied Optics

Applied Optics


  • Editor: James C. Wyant
  • Vol. 46, Iss. 36 — Dec. 20, 2007
  • pp: 8562–8572

Effects of image restoration on automatic acquisition of moving objects in thermal video sequences degraded by the atmosphere

Oren Haik and Yitzhak Yitzhaky  »View Author Affiliations

Applied Optics, Vol. 46, Issue 36, pp. 8562-8572 (2007)

View Full Text Article

Enhanced HTML    Acrobat PDF (1138 KB)

Browse Journals / Lookup Meetings

Browse by Journal and Year


Lookup Conference Papers

Close Browse Journals / Lookup Meetings

Article Tools



We aim to determine the effect of image restoration (deblurring) on the ability to acquire moving objects detected automatically from long-distance thermal video signals. This is done by first restoring the videos using a blind-deconvolution method developed recently, and then examining its effect on the geometrical features of automatically detected moving objects. Results show that for modern (low-noise and high-resolution) thermal imaging devices, the geometrical features obtained from the restored videos better resemble the true properties of the objects. These results correspond to a previous study, which demonstrated that image restoration can significantly improve the ability of human observers to acquire moving objects from long-range thermal videos.

© 2007 Optical Society of America

OCIS Codes
(100.1830) Image processing : Deconvolution
(280.0280) Remote sensing and sensors : Remote sensing and sensors

ToC Category:
Image Processing

Original Manuscript: July 25, 2007
Revised Manuscript: November 1, 2007
Manuscript Accepted: November 6, 2007
Published: December 13, 2007

Oren Haik and Yitzhak Yitzhaky, "Effects of image restoration on automatic acquisition of moving objects in thermal video sequences degraded by the atmosphere," Appl. Opt. 46, 8562-8572 (2007)

Sort:  Year  |  Journal  |  Reset  


  1. W. Hu, T. Tan, L. Wang, and S. Maybank, "A survey on visual surveillance of object motion and behaviors," IEEE Trans. Syst. Man Cybern. , Part C Appl. Rev. 34, 334-352 (2004). [CrossRef]
  2. Y. Dedeoglu, "Moving object detection, tracking and classification for smart video surveillance" (Bilkent University, 2004). NOTE: Master's thesis. Available online at [accessed 28 October 2007]: http://www.cs.bilkent.edu.tr/~yigithan/publications/MScThesis.pdf.
  3. N. S. Kopeika, A System Engineering Approach to Imaging, 2nd ed. (SPIE Press, 1998).
  4. D. L. Fried, "Optical resolution through a randomly inhomogeneous medium for very long and very short exposures," J. Opt. Soc. Am. 56, 1372-1379 (1966). [CrossRef]
  5. N. S. Kopeika, I. Dror, and D. Sadot, "The causes of atmospheric blur: comment on atmospheric scattering effect on spatial resolution of imaging systems," J. Opt. Soc. Am. A 15, 3097-3106 (1998). [CrossRef]
  6. I. Dror and N. S. Kopeika, "Experimental comparison of turbulence MTF and aerosol MTF through the open atmosphere," J. Opt. Soc. Am. A 12, 970-980 (1995). [CrossRef]
  7. Y. Yitzhaky, I. Dror, and N. S. Kopeika, "Restoration of atmospherically-blurred images according to weather-predicted atmospheric modulation transfer function (MTF)," Opt. Eng. 36, 3064-3072 (1997). [CrossRef]
  8. D. Li, R. Mersereau, D. H. Frakes, and M. J. T. Smith, "A new method for suppressing optical turbulence in video," in Proceedings of EUSIPCO (2005).
  9. S. Cheung and C. Kamath, "Robust techniques for background subtraction in urban traffic video," Proc. SPIE 5308, 881-892 (2004). [CrossRef]
  10. A. Strehl and J. K. Aggarwal, "Detecting moving objects in airborne forward looking infra-red sequences," in Proceedings of the IEEE Workshop on Computer Vision Beyond the Visible Spectrum: Methods and Applications (IEEE, 1999), pp. 3-12. [CrossRef]
  11. E. Estalayo, L. Salgado, F. Jaureguizar, and N. Garcia, "Efficient image stabilization and automatic target detection in aerial FLIR sequence," Proc. SPIE 6234, 62340N (2006). [CrossRef]
  12. O. Haik, Y. Lior, D. Nahmani, and Y. Yitzhaky, "Effects of image restoration on acquisition of moving objects from thermal video sequences degraded by the atmosphere," Opt. Eng. 45, 117006 (2006). [CrossRef]
  13. O. Shacham, O. Haik, and Y. Yitzhaky, "Blind restoration of atmospherically degraded images by automatic best step-edge detection," Pattern Recogn. Lett. 28, 2094-2103 (2007). [CrossRef]
  14. A. K. Jain, Fundamentals of Digital Image Processing (Prentice Hall, 1989).
  15. D. Kundur and D. Hatzinakos, "Blind image deconvolution," IEEE Signal Process. Mag. 13, 43-64 (1996). [CrossRef]
  16. A. Jalobeanu, J. Zerubia, and L. Blanc-Feraud, "Bayesian estimation of blur and noise in remote sensing imaging," in Blind image deconvolution: theory and applications, P. Campisi and K. Egiazarian, ed. (CRC Press, 2007).
  17. A. E. Savakis and H. J. Trussell, "Blur identification by residual spectral matching," IEEE Trans. Image Process. 2, 141-151 (1993). [CrossRef] [PubMed]
  18. G. Pavlovic and A. M. Tekalp, "Maximum likelihood parametric blur identification based on a continuous spatial domain model," IEEE Trans. Image Process. 1, 496-504 (1992). [CrossRef] [PubMed]
  19. D. G. Sheppard, H. Bobby, and M. Michael, "Iterative multi-frame super-resolution algorithms for atmospheric-turbulence-degraded imagery," J. Opt. Soc. Am. A 15, 978-992 (1998). [CrossRef]
  20. Q. Zhou and J. K. Aggarwal, "Object tracking in an outdoor environment using fusion of features and cameras," Image Vis. Comput. 24, 1244-1255 (2006). [CrossRef]
  21. A. J. Lipton, H. Fujiyoshi, and R. S. Patil, "Moving target classification and tracking from real-time video," in Proceedings of the IEEE Image Understanding Workshop (IEEE, 1998), pp. 129-136.
  22. M. A. Ali, S. Indupalli, and B. Boufama, "Tracking Multiple People for Video Surveillance," in First International Workshop on Video Processing for Security (2006).
  23. A. Elgammal, D. Harwood, and L. Davis, "Non-parametric model for background subtraction," in Proceedings of IEEE Conference on Computer Vision1843 (IEEE, 2000), pp. 751-767.
  24. G. Baldini, P. Campadelli, D. Cozzi, and R. Lanzarotti, "A simple and robust method for moving target tracking," in Proceedings of the IASTED International Conference on Signal Processing, Pattern Recognition and Applications (SPPRA, 2002), pp. 108-112. [PubMed]
  25. A. Amer, "Voting-based simultaneous tracking of multiple video objects," Proc. SPIE 5022, 500-511 (2003). [CrossRef]
  26. CONTROP precision technologies LTD., "Innovative solutions for surveillance and reconnaissance," http://www.controp.co.il.
  27. H. He and L. P. Kondi, "A super-resolution technique with motion estimation considering atmospheric turbulence," Proc. SPIE 5789, 135-144 (2005). [CrossRef]
  28. D. Li and R. M. Mersereau, "Blur identification based on kurtosis minimization," in Proceedings of the IEEE International Conference on Image Processing (IEEE, 2005), pp. 905-908.
  29. M. Lalonde, S. Foucher, L. Gagnon, E. Pronovost, M. Derenne, and A. Janelle, "A system to automatically track humans and vehicles with a PTZ camera," Proc. SPIE 6575, 657502 (2007). [CrossRef]
  30. Y. Yitzhaky's Home Page at Ben-Gurion University, Israel, http://www.ee.bgu.ac.il/~itzik/VideosAO07/.
  31. B. Bose and E. Grimson, "Learning to use scene context for object classification in surveillance," in Proceedings of the IEEE International Workshop on VS-PETS (IEEE, 2003), pp. 94-101.
  32. Y. Bogomolov, G. Dror, S. Lapchev, E. Rivlin, and M. Rudzsky, "Classification of moving targets based on motion and appearance," in BMVC03 (2003).

Cited By

Alert me when this paper is cited

OSA is able to provide readers links to articles that cite this paper by participating in CrossRef's Cited-By Linking service. CrossRef includes content from more than 3000 publishers and societies. In addition to listing OSA journal articles that cite this paper, citing articles from other participating publishers will also be listed.

« Previous Article  |  Next Article »

OSA is a member of CrossRef.

CrossCheck Deposited