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Journal of the Optical Society of America A

Journal of the Optical Society of America A

| OPTICS, IMAGE SCIENCE, AND VISION

  • Vol. 15, Iss. 2 — Feb. 1, 1998
  • pp: 407–418

Phase unwrapping by means of genetic algorithms

Antonio Collaro, Giorgio Franceschetti, Francesco Palmieri, and Maria Sedes Ferreiro  »View Author Affiliations


JOSA A, Vol. 15, Issue 2, pp. 407-418 (1998)
http://dx.doi.org/10.1364/JOSAA.15.000407


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Abstract

A new phase unwrapping algorithm is presented that implements phase retrieval without any need for preliminary cuts or weights of the wrapped phase diagram. Excellent results on simulated as well as real data validate the proposed technique.

© 1998 Optical Society of America

OCIS Codes
(100.5070) Image processing : Phase retrieval
(110.1220) Imaging systems : Apertures
(120.3180) Instrumentation, measurement, and metrology : Interferometry

History
Original Manuscript: February 19, 1997
Revised Manuscript: June 30, 1997
Manuscript Accepted: August 20, 1997
Published: February 1, 1998

Citation
Antonio Collaro, Giorgio Franceschetti, Francesco Palmieri, and Maria Sedes Ferreiro, "Phase unwrapping by means of genetic algorithms," J. Opt. Soc. Am. A 15, 407-418 (1998)
http://www.opticsinfobase.org/josaa/abstract.cfm?URI=josaa-15-2-407


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References

  1. G. Fornaro, G. Franceschetti, R. Lanari, “Interferometric SAR phase unwrapping using Green’s formulation,” IEEE Trans. Geosci. Remote Sens. 34, 1–8 (1996). [CrossRef]
  2. D. C. Ghiglia, L. A. Romero, “Robust two-dimensional weighted and unweighted phase unwrapping that uses fast transforms and iterative methods,” J. Opt. Soc. Am. A 11, 107–117 (1994). [CrossRef]
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  5. M. D. Prit, J. S. Shipman, “Least-squares two-dimensional phase unwrapping using FFT’s,” IEEE Trans. Geosci. Remote Sens. 32, 706–708 (1994). [CrossRef]
  6. G. Fornaro, G. Franceschetti, R. Lanari, E. Sansosti, M. Tesauro, “Global and local phase unwrapping techniques: a comparison,” J. Opt. Soc. Am. A 14, 2702–2708 (1997). [CrossRef]
  7. Notice that the problem of choosing a constant Φ0 for the reconstruction is common to all phase unwrapping algorithms. Thus the reconstructed phase pattern is always affected by a common bias that must be resolved by other means.
  8. To guarantee a perfect reconstruction, the summation path must be made only of horizontal and vertical segments. This is a direct consequence of the definition of Eq. (8) for the finite difference.
  9. Some of these paths may even generate a phase value corresponding to k=0 when different critical region crossings compensate each other.
  10. G. Rudolph, “Convergence analysis of canonical genetic algorithms,” IEEE Trans. Neural Netw. 5, 96–101 (1994). [CrossRef] [PubMed]
  11. X. F. Qi, F. Palmieri, “Theoretical analysis of evolutionary algorithms with an infinite population size in continuous space. Part II: Analysis of the diversification role of crossover,” IEEE Trans. Neural Netw. 5, 120–129 (1994). [CrossRef] [PubMed]
  12. X. F. Qi, F. Palmieri, “Theoretical analysis of evolutionary algorithms with an infinite population size in continuous space. Part I: Basic properties of selection and mutation,” IEEE Trans. Neural Netw. 5, 102–119 (1994). [CrossRef]
  13. D. Just, R. Bamler, “Phase statistics of interferogram with application to synthetic aperture radar,” Appl. Opt. 33, 4361–4368 (1994). [CrossRef] [PubMed]
  14. The difference between the wrapped phase pattern and its filtered version is saved, wrapped, and added to the final reconstructed unwrapped phase.
  15. At variance with the simulated case, the filtered noise is no longer added to the final result, a usual technique to smooth the final image.

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