Registration and Fusion of Blurred Images

  • Filip Sroubek
  • Jan Flusser
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3211)


We present a maximum a posteriori solution to problems of accurate registration of blurred images and recovery of an original undegraded image. Our algorithm has the advantage that both tasks are performed simultaneously. An efficient implementation scheme of alternating minimizations is presented. A simulation and a real-data experiment demonstrate the superb performance of the algorithm.


Image Fusion Registration Method Fusion Algorithm Registration Error Degraded Image 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Zitová, B., Flusser, J.: Image registration methods: A survey. Image and Vision Computing 21, 977–1000 (2003)CrossRefGoogle Scholar
  2. 2.
    Myles, Z., Lobo, N.V.: Recovering affine motion and defocus blur simultaneously. IEEE Trans. Pattern Analysis and Machine Intelligence 20, 652–658 (1998)CrossRefGoogle Scholar
  3. 3.
    Zhang, Y., Wen, C., Zhang, Y., Soh, Y.C.: Determination of blur and affine combined invariants by normalization. Pattern Recognition 35, 211–221 (2002)zbMATHCrossRefGoogle Scholar
  4. 4.
    Kubota, A., Kodama, K., Aizawa, K.: Registration and blur estimation methods for multiple differently focused images. In: Proceedings International Conference on Image Processing, vol. II, pp. 447–451 (1999)Google Scholar
  5. 5.
    Zhang, Z., Blum, R.: A hybrid image registration technique for a digital camera image fusion application. Information Fusion 2, 135–149 (2001)CrossRefGoogle Scholar
  6. 6.
    Flusser, J., Suk, T., Saic, S.: Recognition of blurred images by the method of moments. IEEE Trans. Image Processing 5, 533–538 (1996)CrossRefGoogle Scholar
  7. 7.
    Flusser, J., Zitová, B.: Combined invariants to linear filtering and rotation. Intl. J. Pattern Recognition Art. Intell. 13, 1123–1136 (1999)CrossRefGoogle Scholar
  8. 8.
    Zitová, B., Kautsky, J., Peters, G., Flusser, J.: Robust detection of significant points in multiframe images. Pattern Recognition Letters 20, 199–206 (1999)zbMATHCrossRefGoogle Scholar
  9. 9.
    Flusser, J., Zitová, B., Suk, T.: Invariant-based registration of rotated and blurred images. In: Tammy, I.S. (ed.) Proceedings IEEE 1999 International Geoscience and Remote Sensing Symposium, pp. 1262–1264. IEEE Computer Society, Los Alamitos (1999)Google Scholar
  10. 10.
    Harikumar, G., Bresler, Y.: Perfect blind restoration of images blurred by multiple filters: Theory and efficient algorithms. IEEE Trans. Image Processing 8, 202–219 (1999)CrossRefGoogle Scholar
  11. 11.
    Pai, H.T., Bovik, A.: On eigenstructure-based direct multichannel blind image restoration. IEEE Trans. Image Processing 10, 1434–1446 (2001)zbMATHCrossRefGoogle Scholar
  12. 12.
    Panci, G., Campisi, P., Colonnese, S., Scarano, G.: Multichannel blind image deconvolution using the bussgang algorithm: Spatial and multiresolution approaches. IEEE Trans. Image Processing 12, 1324–1337 (2003)CrossRefMathSciNetGoogle Scholar
  13. 13.
    Šroubek, F., Flusser, J.: Multichannel blind iterative image restoration. IEEE Trans. Image Processing 12, 1094–1106 (2003)CrossRefGoogle Scholar
  14. 14.
    Woods, N., Galatsanos, N., Katsaggelos, A.: EM-based simultaneous registration, restoration, and interpolation of super-resolved images. In: Image Processing, 2003 Proceedings, vol. 2, pp. 303–306 (2003)Google Scholar
  15. 15.
    Rav-Acha, A., Peleg, S.: Restoration of multiple images with motion blur in different directions. In: IEEE Workshop on Applications of Computer Vision (WACV), pp. 22–27 (2000)Google Scholar
  16. 16.
    Šroubek, F., Flusser, J.: Shift-invariant multichannel blind restoration. In: Proceedings of the 3rd Int’l Symposium on Image and Signal Processing and Analysis, ISPA 2003, Rome, IEEE, Los Alamitos (2003)Google Scholar
  17. 17.
    Aubert, G., Kornprobst, P.: athematical Problems in Image Processing. Springer, New York (2002)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2004

Authors and Affiliations

  • Filip Sroubek
    • 1
  • Jan Flusser
    • 1
  1. 1.Institute of Information Theory and AutomationAcademy of Sciences of the Czech RepublicPraha 8

Personalised recommendations