A Frequency Domain Approach to Registration of Aliased Images with Application to Super-resolution

  • Patrick Vandewalle
  • Sabine Süsstrunk
  • Martin Vetterli
Open Access
Research Article
Part of the following topical collections:
  1. Super-Resolution Imaging: Analysis, Algorithms, and Applications


Super-resolution algorithms reconstruct a high-resolution image from a set of low-resolution images of a scene. Precise alignment of the input images is an essential part of such algorithms. If the low-resolution images are undersampled and have aliasing artifacts, the performance of standard registration algorithms decreases. We propose a frequency domain technique to precisely register a set of aliased images, based on their low-frequency, aliasing-free part. A high-resolution image is then reconstructed using cubic interpolation. Our algorithm is compared to other algorithms in simulations and practical experiments using real aliased images. Both show very good visual results and prove the attractivity of our approach in the case of aliased input images. A possible application is to digital cameras where a set of rapidly acquired images can be used to recover a higher-resolution final image.


Frequency Domain Digital Camera Quantum Information Input Image Practical Experiment 


  1. 1.
    Tsai RY, Huang TS: Multiframe image restoration and registration. In Advances in Computer Vision and Image Processing. Volume 1. JAI Press, Greenwich, Conn, USA; 1984:317–339. chapter 7Google Scholar
  2. 2.
    Vandewalle P, Süsstrunk SE, Vetterli M: Super-resolution images reconstructed from aliased images. In Proceedings of SPIE/IS&T Visual Communications and Image Processing Conference, Proceedings of SPIE. Volume 5150. Edited by: Ebrahimi T, Sikora T. , Lugano, Switzerland; 2003:1398–1405.Google Scholar
  3. 3.
    Vandewalle P, Süsstrunk SE, Vetterli M: Double resolution from a set of aliased images. In Proceedings of SPIE/IS&T Electronic Imaging 2004: Sensors and Camera Systems for Scientific, Industrial, and Digital Photography Applications V, Proceedings of SPIE. Volume 5301. , San Jose, Calif, USA; 2004:374–382.Google Scholar
  4. 4.
    Capel D, Zisserman A: Computer vision applied to super-resolution. IEEE Signal Processing Magazine 2003, 20(3):75–86. 10.1109/MSP.2003.1203211CrossRefGoogle Scholar
  5. 5.
    Keren D, Peleg S, Brada R: Image sequence enhancement using sub-pixel displacements. Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR '88), June 1988, Ann Arbor, Mich, USA 742–746.CrossRefGoogle Scholar
  6. 6.
    Schultz RR, Meng L, Stevenson RL: Subpixel motion estimation for super-resolution image sequence enhancement. Journal of Visual Communication and Image Representation 1998, 9(1):38–50. 10.1006/jvci.1997.0370CrossRefGoogle Scholar
  7. 7.
    Irani M, Peleg S: Improving resolution by image registration. CVGIP: Graphical Models and Image Processing 1991, 53(3):231–239. 10.1016/1049-9652(91)90045-LGoogle Scholar
  8. 8.
    Rajan D, Chaudhuri S, Joshi MV: Multi-objective super-resolution: concepts and examples. IEEE Signal Processing Magazine 2003, 20(3):49–61. 10.1109/MSP.2003.1203209CrossRefGoogle Scholar
  9. 9.
    Joshi MV, Chaudhuri S, Panuganti R: Super-resolution imaging: use of zoom as a cue. Image and Vision Computing 2004, 22(14):1185–1196.CrossRefGoogle Scholar
  10. 10.
    Patti AJ, Sezan MI, Murat Tekalp A: Super-resolution video reconstruction with arbitrary sampling lattices and nonzero aperture time. IEEE Transactions on Image Processing 1997, 6(8):1064–1076. 10.1109/83.605404CrossRefGoogle Scholar
  11. 11.
    Zomet A, Rav-Acha A, Peleg S: Robust super-resolution. Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR '01), December 2001, Kauai, Hawaii, USA 1: 645–650.Google Scholar
  12. 12.
    Farsiu S, Robinson MD, Elad M, Milanfar P: Fast and robust multiframe super-resolution. IEEE Transactions on Image Processing 2004, 13(10):1327–1344. 10.1109/TIP.2004.834669CrossRefGoogle Scholar
  13. 13.
    Elad M, Feuer A: Restoration of a single super-resolution image from several blurred, noisy, and undersampled measured images. IEEE Transactions on Image Processing 1997, 6(12):1646–1658. 10.1109/83.650118CrossRefGoogle Scholar
  14. 14.
    Borman S, Stevenson RL: Spatial resolution enhancement of low-resolution image sequences—a comprehensive review with directions for future research. Laboratory for Image and Signal Analysis (LISA), University of Notre Dame, Notre Dame, Ind, USA; 1998. Online available:Google Scholar
  15. 15.
    Park SC, Park MK, Kang MG: Super-resolution image reconstruction: a technical overview. IEEE Signal Processing Magazine 2003, 20(3):21–36. 10.1109/MSP.2003.1203207CrossRefGoogle Scholar
  16. 16.
    Zitová B, Flusser J: Image registration methods: a survey. Image and Vision Computing 2003, 21(11):977–1000. 10.1016/S0262-8856(03)00137-9CrossRefGoogle Scholar
  17. 17.
    Reddy BS, Chatterji BN: An FFT-based technique for translation, rotation, and scale-invariant image registration. IEEE Transactions on Image Processing 1996, 5(8):1266–1271. 10.1109/83.506761CrossRefGoogle Scholar
  18. 18.
    Marcel B, Briot M, Murrieta R: Calcul de translation et rotation par la transformation de Fourier. Traitement du Signal 1997, 14(2):135–149.zbMATHGoogle Scholar
  19. 19.
    Kim SP, Su W-Y: Subpixel accuracy image registration by spectrum cancellation. Proceedings of IEEE International Conference Acoustics, Speech, Signal Processing (ICASSP '93), April 1993, Minneapolis, Minn, USA 5: 153–156.Google Scholar
  20. 20.
    Stone HS, Orchard MT, Chang E-C, Martucci SA: A fast direct Fourier-based algorithm for subpixel registration of images. IEEE Transactions on Geoscience and Remote Sensing 2001, 39(10):2235–2243. 10.1109/36.957286CrossRefGoogle Scholar
  21. 21.
    Foroosh H, Zerubia JB, Berthod M: Extension of phase correlation to subpixel registration. IEEE Transactions on Image Processing 2002, 11(3):188–200. 10.1109/83.988953CrossRefGoogle Scholar
  22. 22.
    Lucchese L, Cortelazzo GM: A noise-robust frequency domain technique for estimating planar roto-translations. IEEE Transactions on Signal Processing 2000, 48(6):1769–1786. 10.1109/78.845934CrossRefGoogle Scholar
  23. 23.
    Fischler MA, Bolles RC: Random sample consensus: a paradigm for model fitting with applications to image analysis and automated cartography. Communications of the ACM 1981, 24(6):381–395. 10.1145/358669.358692MathSciNetCrossRefGoogle Scholar
  24. 24.
    Bergen JR, Anandan P, Hanna KJ, Hingorani R: Hierarchical model-based motion estimation. Proceedings of 2nd European Conference on Computer Vision (ECCV '92), May 1992, Santa Margherita Ligure, Italy, Lecture Notes in Computer Science 237–252.Google Scholar
  25. 25.
    Irani M, Rousso B, Peleg S: Computing occluding and transparent motions. International Journal of Computer Vision 1994, 12(1):5–16.CrossRefGoogle Scholar
  26. 26.
    Gluckman J: Gradient field distributions for the registration of images. Proceedings of IEEE International Conference on Image Processing (ICIP '03), September 2003, Barcelona, Spain 3: 691–694.Google Scholar
  27. 27.
    Papoulis A: Generalized sampling expansion. IEEE Transactions on Circuits Systems 1977, 24(11):652–654. 10.1109/TCS.1977.1084284MathSciNetCrossRefGoogle Scholar
  28. 28.
    Farsiu S, Robinson MD, Milanfar P: MDSP resolution enhancement software. 2004. Online available:Google Scholar
  29. 29.
    International Organization for Standardization : ISO 12233:2000—Photography—Electronic still picture cameras—Resolution measurements. 2000.Google Scholar
  30. 30.
  31. 31.
    Schwab M, Karrenbach M, Claerbout J: Making scientific computations reproducible. Computing in Science & Engineering 2000, 2(6):61–67.CrossRefGoogle Scholar

Copyright information

© Patrick Vandewalle et al. 2006

This article is published under license to BioMed Central Ltd. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Authors and Affiliations

  • Patrick Vandewalle
    • 1
  • Sabine Süsstrunk
    • 1
  • Martin Vetterli
    • 1
    • 2
  1. 1.Ecole Polytechnique Fédéral de LausanneSchool of Computer and Communication SciencesLausanneSwitzerland
  2. 2.Department of Electrical Engineering and Computer SciencesUniversity of CaliforniaBerkeleyUSA

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