Skip to main content

Super-Resolution for Synthetic Zooming

Abstract

Optical zooming is an important feature of imaging systems. In this paper, we investigate a low-cost signal processing alternative to optical zooming—synthetic zooming by super-resolution (SR) techniques. Synthetic zooming is achieved by registering a sequence of low-resolution (LR) images acquired at varying focal lengths and reconstructing the SR image at a larger focal length or increased spatial resolution. Under the assumptions of constant scene depth and zooming speed, we argue that the motion trajectories of all physical points are related to each other by a unique vanishing point and present a robust technique for estimating itsD coordinate. Such a line-geometry-based registration is the foundation of SR for synthetic zooming. We address the issue of data inconsistency arising from the varying focal length of optical lens during the zooming process. To overcome the difficulty of data inconsistency, we propose a two-stage Delaunay-triangulation-based interpolation for fusing the LR image data. We also present a PDE-based nonlinear deblurring to accommodate the blindness and variation of sensor point spread functions. Simulation results with real-world images have verified the effectiveness of the proposed SR techniques for synthetic zooming.

References

  1. 1.

    Holst GC: CCD Arrays, Cameras and Displays. SPIE-International Society for Optical Engine, Bellingham, Wash, USA; 1998.

    Google Scholar 

  2. 2.

    Choi E, Choi J, Kang MG: Super-resolution approach to overcome physical limitations of imaging sensors: an overview. International Journal of Imaging Systems and Technology 2004, 14(2):36-46. Special issue on high resolution image reconstruction 10.1002/ima.20006

    MathSciNet  Article  Google Scholar 

  3. 3.

    Farsiu S, Robinson D, Elad M, Milanfar P: Advances and challenges in super-resolution. International Journal of Imaging Systems and Technology 2004, 14(2):47–57. Special issue on high resolution image reconstruction 10.1002/ima.20007

    Article  Google Scholar 

  4. 4.

    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 

  5. 5.

    Wen Y-W, Ng MK, Ching W-K: High-resolution image reconstruction from rotated and translated low-resolution images with multisensors. International Journal of Imaging Systems and Technology 2004, 14(2):75–83. Special issue on high resolution image reconstruction 10.1002/ima.20010

    Article  Google Scholar 

  6. 6.

    Nguyen N, Milanfar P, Golub G: A computationally efficient super-resolution image reconstruction algorithm. IEEE Transactions on Image Processing 2001, 10(4):573–583. 10.1109/83.913592

    Article  Google Scholar 

  7. 7.

    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 

  8. 8.

    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 

  9. 9.

    Schultz RR, Stevenson RL: Extraction of high-resolution frames from video sequences. IEEE Transactions on Image Processing 1996, 5(6):996–1011. 10.1109/83.503915

    Article  Google Scholar 

  10. 10.

    Alvarez LD, Mateos J, Molina R, Katsaggelos AK: High-resolution images from compressed low-resolution video: Motion estimation and observable pixels. International Journal of Imaging Systems and Technology 2004, 14(2):58–66. Special issue on high resolution image reconstruction 10.1002/ima.20008

    Article  Google Scholar 

  11. 11.

    Gunturk BK, Altunbasak Y, Mersereau RM: Super-resolution reconstruction of compressed video using transform-domain statistics. IEEE Transactions on Image Processing 2004, 13(1):33–43. 10.1109/TIP.2003.819221

    Article  Google Scholar 

  12. 12.

    Jin R, Qi Y, Hauptmann A: A probabilistic model for camera zoom detection. Proceedings of IEEE 16th International Conference on Pattern Recognition (ICPR '02), August 2002, Quebec City, Quebec, Canada 3: 859–862.

    Article  Google Scholar 

  13. 13.

    Kingslake R: Applied Optics and Optical Engineering. Academic Press, New York, NY, USA; 1965.

    Google Scholar 

  14. 14.

    Lertrattanapanich S, Bose NK: High resolution image formation from low resolution frames using Delaunay triangulation. IEEE Transactions on Image Processing 2002, 11(12):1427–1441. 10.1109/TIP.2002.806234

    MathSciNet  Article  Google Scholar 

  15. 15.

    Osher S, Rudin LI: Feature-oriented image-enhancement using shock filters. SIAM Journal on Numerical Analysis 1990, 27(4):919–940. 10.1137/0727053

    Article  Google Scholar 

  16. 16.

    Alvarez L, Mazorra L: Signal and image restoration using shock filters and anisotropic diffusion. SIAM Journal on Numerical Analysis 1994, 31(2):590–605. 10.1137/0731032

    MathSciNet  Article  Google Scholar 

  17. 17.

    El-Fallah AI, Ford GE: Mean curvature evolution and surface area scaling in image filtering. IEEE Transactions on Image Processing 1997, 6(5):750–753. 10.1109/83.568931

    Article  Google Scholar 

  18. 18.

    Brown LG: A survey of image registration techniques. ACM Computing Surveys 1992, 24(4):325–376. 10.1145/146370.146374

    Article  Google Scholar 

  19. 19.

    Murat Tekalp A: Digital Video Processing. Prentice-Hall, Englewood Cliffs, NJ, USA; 1995.

    Google Scholar 

  20. 20.

    Lowe DG: Object recognition from local scale-invariant features. Proceedings of IEEE 7th International Conference on Computer Vision (ICCV '99), September 1999, Kerkyra, Greece 2: 1150–1157.

    Google Scholar 

  21. 21.

    Schmid C, Mohr R: Local grayvalue invariants for image retrieval. IEEE Transactions on Pattern Analysis and Machine Intelligence 1997, 19(5):530–535. 10.1109/34.589215

    Article  Google Scholar 

  22. 22.

    Shi J, Tomasi C: Good features to track. Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR '94), June 1994, Seattle, Wash, USA 593–600.

    Google Scholar 

  23. 23.

    Yen C, Burt PJ, Xu X: Local correlation measures from motion analysis: a comparative study. Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR '82), June 1982, Las Vegas, Nev, USA 269–274.

    Google Scholar 

  24. 24.

    Gu Y-H, Tjahjadi T: Coarse-to-fine planar object identification using invariant curve features and B-spline modeling. Pattern Recognition 2000, 33(9):1411–1422. 10.1016/S0031-3203(99)00131-4

    Article  Google Scholar 

  25. 25.

    Nelder JA, Mead R: A simplex method for function minimization. The Computer Journal 1965, 7(4):308–313.

    MathSciNet  Article  Google Scholar 

  26. 26.

    Alvarez L, Lions P-L, Morel J-M: Image selective smoothing and edge detection by nonlinear diffusion. II. SIAM Journal on Numerical Analysis 1992, 29(3):845–866. 10.1137/0729052

    MathSciNet  Article  Google Scholar 

  27. 27.

    Catté F, Lions P-L, Morel J-M, Coll T: Image selective smoothing and edge detection by nonlinear diffusion. SIAM Journal on Numerical Analysis 1992, 29(1):182–193. 10.1137/0729012

    MathSciNet  Article  Google Scholar 

  28. 28.

    Gilboa G, Sochen N, Zeevi YY: Forward-and-backward diffusion processes for adaptive image enhancement and denoising. IEEE Transactions on Image Processing 2002, 11(7):689–703. 10.1109/TIP.2002.800883

    Article  Google Scholar 

  29. 29.

    Perona P, Malik J: Scale-space and edge detection using anisotropic diffusion. IEEE Transactions on Pattern Analysis and Machine Intelligence 1990, 12(7):629–639. 10.1109/34.56205

    Article  Google Scholar 

  30. 30.

    Olver PJ, Sapiro G, Tannenbaum A: Affine invariant detection: edge maps, anisotropic diffusion, and active contours. Acta Applicandae Mathematicae 1999, 59(1):45–77. 10.1023/A:1006295328209

    MathSciNet  Article  Google Scholar 

Download references

Author information

Affiliations

Authors

Corresponding author

Correspondence to Xin Li.

Rights and permissions

Reprints and Permissions

About this article

Cite this article

Li, X. Super-Resolution for Synthetic Zooming. EURASIP J. Adv. Signal Process. 2006, 058195 (2006). https://doi.org/10.1155/ASP/2006/58195

Download citation

Keywords

  • Deblurring
  • Focal Length
  • Blindness
  • Point Spread Function
  • Motion Trajectory