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

Abstract

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.

References

  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 7

    Google Scholar 

  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. 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. Capel D, Zisserman A: Computer vision applied to super-resolution. IEEE Signal Processing Magazine 2003, 20(3):75–86. 10.1109/MSP.2003.1203211

    Article  Google Scholar 

  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.

    Chapter  Google Scholar 

  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.1997.0370

    Article  Google Scholar 

  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-L

    Google Scholar 

  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.2003.1203209

    Article  Google Scholar 

  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.

    Article  Google Scholar 

  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. 10.1109/83.605404

    Article  Google Scholar 

  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. 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.2004.834669

    Article  Google Scholar 

  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. 10.1109/83.650118

    Article  Google Scholar 

  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.https://doi.org/www.nd.edu/~sborman/publications/ Online available:

    Google Scholar 

  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.2003.1203207

    Article  Google Scholar 

  16. Zitová B, Flusser J: Image registration methods: a survey. Image and Vision Computing 2003, 21(11):977–1000. 10.1016/S0262-8856(03)00137-9

    Article  Google Scholar 

  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. 10.1109/83.506761

    Article  Google Scholar 

  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.

    MATH  Google Scholar 

  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. 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. 10.1109/36.957286

    Article  Google Scholar 

  21. Foroosh H, Zerubia JB, Berthod M: Extension of phase correlation to subpixel registration. IEEE Transactions on Image Processing 2002, 11(3):188–200. 10.1109/83.988953

    Article  Google Scholar 

  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. 10.1109/78.845934

    Article  Google Scholar 

  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. 10.1145/358669.358692

    Article  MathSciNet  Google Scholar 

  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. Irani M, Rousso B, Peleg S: Computing occluding and transparent motions. International Journal of Computer Vision 1994, 12(1):5–16.

    Article  Google Scholar 

  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. Papoulis A: Generalized sampling expansion. IEEE Transactions on Circuits Systems 1977, 24(11):652–654. 10.1109/TCS.1977.1084284

    Article  MathSciNet  Google Scholar 

  28. Farsiu S, Robinson MD, Milanfar P: MDSP resolution enhancement software. 2004.https://doi.org/www.soe.ucsc.edu/~milanfar/SR-Software.htm Online available:

    Google Scholar 

  29. International Organization for Standardization : ISO 12233:2000—Photography—Electronic still picture cameras—Resolution measurements. 2000.

    Google Scholar 

  30. https://doi.org/lcavwww.epfl.ch/reproducible_research/VandewalleSV05/

  31. Schwab M, Karrenbach M, Claerbout J: Making scientific computations reproducible. Computing in Science & Engineering 2000, 2(6):61–67.

    Article  Google Scholar 

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Open Access This article is distributed under the terms of the Creative Commons Attribution 2.0 International License ( https://creativecommons.org/licenses/by/2.0 ), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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Vandewalle, P., Süsstrunk, S. & Vetterli, M. A Frequency Domain Approach to Registration of Aliased Images with Application to Super-resolution. EURASIP J. Adv. Signal Process. 2006, 071459 (2006). https://doi.org/10.1155/ASP/2006/71459

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  • DOI: https://doi.org/10.1155/ASP/2006/71459

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