Depth Recovery from Motion and Defocus Blur

  • Huei-Yung Lin
  • Chia-Hong Chang
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4142)


Finding the distance of an object in a scene from intensity images is an essential problem in many applications. In this work, we present a novel method for depth recovery from a single motion and defocus blurred image. Under the assumption of uniform linear motion between the camera and the scene during finite exposure time, both the pinhole model and the camera with a finite aperture are considered. The blur extent is estimated by intensity profile analysis and focus measurement of the deblurred images. The proposed method has been verified experimentally using edge images.


Focus Position Camera Model Aperture Diameter Motion Blur Photometric Stereo 
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  1. 1.
    Trucco, E., Verri, A.: Introductory Techniques for 3-D Computer Vision. Prentice-Hall, Englewood Cliffs (1998)zbMATHGoogle Scholar
  2. 2.
    Subbarao, M., Surya, G.: Depth from defocus: A spatial domain approach. International Journal of Computer Vision 13, 271–294 (1994)CrossRefGoogle Scholar
  3. 3.
    Rajagopalan, A.N., Chaudhuri, S.: Simultaneous depth recovery and image restoration from defocused images. In: CVPR, pp. 1348–1353. IEEE Computer Society, Los Alamitos (1999)Google Scholar
  4. 4.
    Favaro, P., Soatto, S.: A geometric approach to shape from defocus. IEEE Trans. Pattern Analysis and Machine Intelligence 27, 406–417 (2005)CrossRefGoogle Scholar
  5. 5.
    Deschênes, F., Ziou, D., Fuchs, P.: An unified approach for a simultaneous and cooperative estimation of defocus blur and spatial shifts. Image and Vision Computing 22, 35–57 (2004)CrossRefGoogle Scholar
  6. 6.
    Rajagopalan, A.N., Chaudhuri, S., Mudenagudi, U.: Depth estimation and image restoration using defocused stereo pairs. IEEE Trans. Pattern Anal. and Mach. Intell. 26, 1521–1525 (2004)CrossRefGoogle Scholar
  7. 7.
    Banham, M., Katsaggelos, A.: Digital image restoration. IEEE Signal Processing Magazine 14, 24–41 (1997)CrossRefGoogle Scholar
  8. 8.
    Kang, S., Min, J., Paik, J.: Segmentation-based spatially adaptive motion blur removal and its application to surveillance systems. In: International Conference on Image Processing, vol. I, pp. 245–248 (2001)Google Scholar
  9. 9.
    Brostow, G., Essa, I.: Image-based motion blur for stop motion animation. In: SIGGRAPH 2001 Conference Proceedings, ACM SIGGRAPH, pp. 561–566 (2001)Google Scholar
  10. 10.
    Ben-Ezra, M., Nayar, S.: Motion-based motion deblurring. IEEE Trans. Pattern Analysis and Machine Intelligence 26, 689–698 (2004)CrossRefGoogle Scholar
  11. 11.
    Lin, H.Y.: Vehicle speed detection and identification from a single motion blurred image. In: WACV/MOTION, pp. 461–467. IEEE Computer Society, Los Alamitos (2005)Google Scholar
  12. 12.
    Slepian, D.: Restoration of photographs blurred by image motion. Bell System Tech. 46, 2353–2362 (1967)Google Scholar
  13. 13.
    Sondhi, M.: Image restoration: The removal of spatially invariant degradations. Proceedings of IEEE 60, 842–853 (1972)CrossRefGoogle Scholar
  14. 14.
    Yitzhaky, Y., Mor, I., Lantzman, A., Kopeika, N.: Direct method for restoration of motion blurred images. Journal of the Optical Society of America 15, 1512–1519 (1998)Google Scholar
  15. 15.
    Fox, J.: Range from translational motion blurring. In: IEEE Computer Vision and Pattern Recognition, pp. 360–365 (1988)Google Scholar
  16. 16.
    Favaro, P., Burger, M., Soatto, S.: Scene and motion reconstruction from defocused and motion-blurred images via anisotropic diffusion. In: Pajdla, T., Matas, J(G.) (eds.) ECCV 2004. LNCS, vol. 3021, pp. 257–269. Springer, Heidelberg (2004)CrossRefGoogle Scholar
  17. 17.
    Lai, S.H., Fu, C.W., Chang, S.: A generalized depth estimation algorithm with a single image. IEEE Trans. Pattern Analy. and Mach. Intell. 14, 405–411 (1992)CrossRefGoogle Scholar
  18. 18.
    Asada, N., Fujiwara, H., Matsuyama, T.: Edge and depth from focus. International Journal of Computer Vision 26, 153–163 (1998)CrossRefGoogle Scholar
  19. 19.
    Ens, J., Lawrence, P.: An investigation of methods for determining depth from focus. IEEE Trans. Pattern Analy. and Mach. Intell. 15, 97–108 (1993)CrossRefGoogle Scholar
  20. 20.
    Hecht, E.: Optics. Addison-Wesley, Reading (2002)Google Scholar
  21. 21.
    Subbarao, M., Choi, T., Nikzad, A.: Focusing techniques. Optical Engineering 32, 2824–2836 (1993)CrossRefGoogle Scholar
  22. 22.
    Horn, B.: Robot Vision. MIT Press, Cambridge (1986)Google Scholar
  23. 23.
    Nayar, S., Nakagawa, Y.: Shape from focus. IEEE Trans. Pattern Analysis and Machine Intelligence 16, 824–831 (1994)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Huei-Yung Lin
    • 1
  • Chia-Hong Chang
    • 1
  1. 1.Department of Electrical EngineeringNational Chung Cheng UniversityChia-YiTaiwan, R.O.C.

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