Recent Advances in Space-Variant Deblurring and Image Stabilization

  • Michal ŠorelEmail author
  • Filip Šroubek
  • Jan Flusser
Part of the NATO Science for Peace and Security Series B: Physics and Biophysics book series (NAPSB)


The blur caused by camera motion is a serious problem in many areas of optical imaging such as remote sensing, aerial reconnaissance or digital photogra phy. As a rule, this problem occurs when low ambient light conditions prevent an imaging system from using sufficiently short exposure times, resulting in a blurred image due to the relative motion between a scene and the imaging system. For exam ple, the cameras attached to airplanes and helicopters are blurred by the forward motion of the aircraft and vibrations. Similarly when taking photographs by hand under dim lighting conditions, camera shake leads to objectionable blur. Producers of imaging systems introduce compensation mechanisms such as gyroscope gim bals in the case of aerial sensing or optical image stabilization systems in the case of digital cameras. These solutions partially remove the blur at the expense of higher cost, weight and energy consumption. Recent advances in image processing make it possible to remove the blur in software. This chapter reviews the image processing techniques we can use for this purpose, discusses the achievable performance and presents some promising results achieved by the authors.


Camera shake image stabilization image registration space-variant restoration deblurring blind deconvolution point spread function regularization 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    M. R. Banham and A. K. Katsaggelos. Digital image restoration. IEEE Signal Process. Mag., 14(2):24–41, March 1997.CrossRefGoogle Scholar
  2. 2.
    R. Fergus, B. Singh, A. Hertzmann, S. T. Roweis, and W. T. Freeman. Removing camera shake from a single photograph. ACM Trans. Graphics, SIGGRAPH 2006 Conf. Proc., Boston, MA, 25:787–794, 2006.Google Scholar
  3. 3.
    M. A. Fischler and R. C. Bolles. Random sample consensus: A paradigm for model fitting with applications to image analysis and automated cartography. Commun. ACM, 24(6):381–395, 1981.CrossRefGoogle Scholar
  4. 4.
    R. Hartley and A. Zisserman. Multiple View Geometry in Computer Vision. Cambridge Uni versity, Cambridge, 2nd edition, 2003.Google Scholar
  5. 5.
    S. H. Lim and D. A. Silverstein. Method for deblurring an image. US Patent Application, Pub. No. US2006/0187308 A1, August 24 2006.Google Scholar
  6. 6.
    J. Miskin and D. MacKay. Ensemble learning for blind image separation and deconvolution. In M. Girolami (Ed.), Advances in independent component analysis. Berlin: Springer-Verlag, (pp. 123–141), 2000.Google Scholar
  7. 7.
    Q. R. Razligh and N. Kehtarnavaz. Image blur reduction for cell-phone cameras via adaptive tonal correction. pages I: 113–116, 2007.Google Scholar
  8. 8.
    L. I. Rudin, S. Osher, and E. Fatemi. Nonlinear total variation based noise removal algorithms. Physica D, 60:259–268, 1992.CrossRefGoogle Scholar
  9. 9.
    Qi Shan, J. Jia, and A. Agarwala. High-quality motion deblurring from a single image. ACM Trans. Graphics (SIGGRAPH), 27(3) 2008.Google Scholar
  10. 10.
    F. Šroubek and J. Flusser. Multichannel blind deconvolution of spatially misaligned images. IEEE Trans. Image Process., 14(7):874–883, July 2005.CrossRefGoogle Scholar
  11. 11.
    M. Tico, M. Trimeche, and M. Vehvilainen. Motion blur identification based on differently exposed images. In Proc. IEEE Int. Conf. Image Process., pp. 2021–2024, 2006.Google Scholar
  12. 12.
    A. Tikhonov and V. Arsenin. Solution of Ill-Posed Problems. Wiley, New York, 1977.Google Scholar
  13. 13.
    D. Tschumperlé and R. Deriche. Vector-valued image regularization with pdes: A common framework for different applications. IEEE Trans. Pattern Anal. Mach. Intell., 27(4):506–517, 2005.CrossRefGoogle Scholar
  14. 14.
    M. Šorel and J. Flusser. Space-variant restoration of images degraded by camera motion blur. IEEE Trans. Image Process., 17(2):105–116, February 2008.CrossRefGoogle Scholar
  15. 15.
    L. Yuan, J. Sun, L. Quan, and H.-Y. Shum. Image deblurring with blurred/noisy image pairs. In SIGGRAPH '07: ACM SIGGRAPH 2007 papers, p. 1, ACM, New York, 2007.Google Scholar
  16. 16.
    L. Yuan, J. Sun, L. Quan, and H.-Y. Shum. Progressive inter-scale and intra-scale non-blind image deconvolution. ACM Trans. Graphics (SIGGRAPH), 2008.Google Scholar

Copyright information

© Springer Science + Business Media B.V. 2009

Authors and Affiliations

  1. 1.Institute of Information Theory and Automation of the ASCRCzech Republic

Personalised recommendations