OSA's Digital Library

Virtual Journal for Biomedical Optics

Virtual Journal for Biomedical Optics

| EXPLORING THE INTERFACE OF LIGHT AND BIOMEDICINE

  • Editor: Gregory W. Faris
  • Vol. 3, Iss. 6 — Jun. 17, 2008

Super-resolution via iterative phase retrieval for blurred and saturated biological images

Eran Gur, Vassilios Sarafis, Igor Falat, Frantisek Vacha, Martin Vacha, and Zeev Zalevsky  »View Author Affiliations


Optics Express, Vol. 16, Issue 11, pp. 7894-7903 (2008)
http://dx.doi.org/10.1364/OE.16.007894


View Full Text Article

Enhanced HTML    Acrobat PDF (314 KB) Open Access





Browse Journals / Lookup Meetings

Browse by Journal and Year


   


Lookup Conference Papers

Close Browse Journals / Lookup Meetings

Article Tools

Share
Citations

Abstract

One of the most fascinating problems addressed today is retrieving high-resolution data of blurred images obtained from biological objects. In most cases the research relays either on a priory knowledge of the image nature or a large number of images (either of the same object or of different objects obtained by the same imaging setup). If saturation is added to the blurring, most algorithms fail to sharpen the image and in some cases researchers decline to use such images as an input. In this work a single captured blurred and saturated image is given with no a priori knowledge except of the fact that the primary blurring is due to defocused imaging setup. The authors suggest a novel three-stage approach for retrieving higher resolution data from the intensity distribution of the blurred and saturated image. The core of the process is the phase retrieval algorithm suggested by Gerchberg and Saxton in 1972. The new method is explained in details and the algorithm is tested numerically and experimentally on several images to show the improvement in the sharpness of the spatial details.

© 2008 Optical Society of America

OCIS Codes
(100.3010) Image processing : Image reconstruction techniques
(100.6640) Image processing : Superresolution
(170.3880) Medical optics and biotechnology : Medical and biological imaging
(110.1455) Imaging systems : Blind deconvolution

ToC Category:
Image Processing

History
Original Manuscript: November 9, 2007
Revised Manuscript: December 24, 2007
Manuscript Accepted: January 10, 2008
Published: May 19, 2008

Virtual Issues
Vol. 3, Iss. 6 Virtual Journal for Biomedical Optics

Citation
Eran Gur, Vassilios Sarafis, Igor Falat, Frantisek Vacha, Martin Vacha, and Zeev Zalevsky, "Super-resolution via iterative phase retrieval for blurred and saturated biological images," Opt. Express 16, 7894-7903 (2008)
http://www.opticsinfobase.org/vjbo/abstract.cfm?URI=oe-16-11-7894


Sort:  Author  |  Year  |  Journal  |  Reset  

References

  1. S. Bellini, Blind deconvolution, S. Haykin, ed. (1994) Chap. 2, pp. 8-55.
  2. N. K. Bose, "Wavelet-based blind super resolution from video sequence and in MRI," Pennsylvania State University - Final Report (2005).
  3. W. T. Freeman, T. R. Jones, and E. C. Pasztora, "Example-based super-rsolution," IEEE Comput. Graphics Appl. 22, 56-65 (2002). [CrossRef]
  4. H. Chang, D-Y Yeung, and Y. Xiong, "Super-resolution through neighbor embedding," in Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR '04), vol. 1, pp. I-275-I-282, Washington, DC, USA, June-July 2004.
  5. M. Elad and A. Feuer, "Restoration of a single super resolution image from several blurred, noisy, and under sampled measured images," IEEE Trans. Image Process. 6, 1646-1658 (1997). [CrossRef] [PubMed]
  6. A. Zomet and S. Peleg, "Multi-sensor super-resolution," IEEE workshop on Applications of Computer Vision (WACV02), 27-31 (2002).
  7. D. Rajan, S. Chaudhuri, and M. V. Joshi, "Multi-objective super resolution: concepts and examples," IEEE Signal Process. Mag. 20, 49-61 (2003). [CrossRef]
  8. M. K. Ng and N. K. Bose, "Mathematical analysis of super-resolution methodology," IEEE Signal Process. Mag. 20, 62-74 (2003). [CrossRef]
  9. D. Capel and A. Zisserman, "Computer vision applied to super resolution," IEEE Signal Process. Mag. 20, 75- 86 (2003). [CrossRef]
  10. R. W. Gerchberg and W. O. Saxton, "A practical algorithm for determination of phase from image and diffraction plane picture," Optik (Stuttgart) 35, 237-246 (1972).
  11. J. R. Feinup, "Phase retrieval algorithms - a comparison," Appl. Opt. 21, 2758-2769 (1982). [CrossRef]
  12. R. W. Gerchberg, "Super-resolution through error energy reduction," Optica Acta 21, 709-720 (1974). [CrossRef]
  13. A. Papoulis, "A new algorithm in spectral analysis and band-limited extrapolation," IEEE Trans. Circuits Syst. 22, 735-742 (1975). [CrossRef]
  14. E. Gur and Z. Zalevsky, "Iterative single-image digital super-resolution using partial high-resolution data," Lecture Notes in Engineering and Computer Science,  WCE2007, 630-634 (2007).
  15. S. Kirkpatrick, C. D. GelattJr., and M. P. Vecchi, "Optimization by simulated annealing," Science 220, 671-679 (1983). [CrossRef] [PubMed]
  16. J. M. Rodenburg, "The phase problem, microdiffraction and wavelength-limited resolution," ltramicroscopy27, 413-422 (1989). [CrossRef]
  17. G. Haberlandt, "Vergleichende anatomie des assimilatorischen gewebesystems bei pflanzen," Jahrbuch der Wissenschaftlichen Botanik 13, 74-188 (1881).
  18. E. Williams, "Fine structure of vascular and epidermal plastids of the mature maize leaf," Protoplasma 79, 395-400 (1974). [CrossRef]

Cited By

Alert me when this paper is cited

OSA is able to provide readers links to articles that cite this paper by participating in CrossRef's Cited-By Linking service. CrossRef includes content from more than 3000 publishers and societies. In addition to listing OSA journal articles that cite this paper, citing articles from other participating publishers will also be listed.


« Previous Article  |  Next Article »

OSA is a member of CrossRef.

CrossCheck Deposited