OSA's Digital Library

Applied Optics

Applied Optics

APPLICATIONS-CENTERED RESEARCH IN OPTICS

  • Vol. 38, Iss. 20 — Jul. 10, 1999
  • pp: 4345–4353

Parallel-distributed blind deconvolution based on a self-organizing neural network

Ning Wang, Yenwei Chen, Zensho Nakao, and Shinichi Tamura  »View Author Affiliations


Applied Optics, Vol. 38, Issue 20, pp. 4345-4353 (1999)
http://dx.doi.org/10.1364/AO.38.004345


View Full Text Article

Enhanced HTML    Acrobat PDF (1698 KB)





Browse Journals / Lookup Meetings

Browse by Journal and Year


   


Lookup Conference Papers

Close Browse Journals / Lookup Meetings

Article Tools

Share
Citations

Abstract

A parallel-distributed blind deconvolution method based on a self-organizing neural network is introduced. A large degraded image is segmented into smaller subpatterns. Each subpattern can be used to get a blur function. Moreover, we propose a two-step unsupervised learning method in the self-organizing neural network. The two-step learning method includes parallel learning and series learning operations. The series learning operation is similar to a typical learning operation in the self-organizing neural network. The parallel learning operation is used as a positive perturbation to let the learning operation leave a local minimum. Several improved blur functions can be estimated from the different subpatterns, and the optimized blur function is evolved by use of a genetic algorithm. As the blur function is estimated, the source image of the large degraded image can be easily restored by use of a Wiener-type filter or other deconvolution methods. Computer simulations show that the proposed parallel-distributed blind deconvolution method gives good reconstruction and that the two-step learning method in the self-organizing neural network can promote learning. Since the main computational cost is dependent on the size of the subpattern, the proposed method is effective for the restoration of the large image.

© 1999 Optical Society of America

OCIS Codes
(100.1830) Image processing : Deconvolution
(100.3020) Image processing : Image reconstruction-restoration
(100.3190) Image processing : Inverse problems
(200.4260) Optics in computing : Neural networks

History
Original Manuscript: July 16, 1998
Revised Manuscript: April 13, 1999
Published: July 10, 1999

Citation
Ning Wang, Yenwei Chen, Zensho Nakao, and Shinichi Tamura, "Parallel-distributed blind deconvolution based on a self-organizing neural network," Appl. Opt. 38, 4345-4353 (1999)
http://www.opticsinfobase.org/ao/abstract.cfm?URI=ao-38-20-4345


Sort:  Author  |  Year  |  Journal  |  Reset  

References

  1. H. Stark, Image Recovery (Academic, San Diego, Calif., 1987).
  2. P. A. Jansson, Deconvolution of Image and Spectra (Academic, San Diego, Calif., 1996).
  3. T. G. Stockham, T. M. Cannon, R. B. Ingebretson, “Blind deconvolution through digital signal processing,” Proc. IEEE 63, 678–692 (1975). [CrossRef]
  4. J. Biemond, F. G. V. Putten, J. W. Woods, “Identification and restoration of images with symmetric noncausal blurs,” IEEE Trans. Circuits Syst. 23, 385–394 (1988). [CrossRef]
  5. A. K. Katsaggelos, “Iterative image restoration algorithms,” Opt. Eng. 28, 735–748 (1989). [CrossRef]
  6. G. R. Ayers, J. C. Dainty, “Iterative blind deconvolution method and its applications,” Opt. Lett. 13, 547–549 (1988). [CrossRef]
  7. B. C. McCallum, “Blind deconvolution by simulated annealing,” Opt. Commun. 75, 547–549 (1988).
  8. Y. W. Chen, Z. Nakao, K. Arakaki, S. Tamura, “Blind deconvolution based on genetic algorithms,” IEICE Trans. Electron. E80-A, 2603–2607 (1997).
  9. Y. T. Zhou, R. Chellappa, A. Vaid, B. K. Jenkins, “Image restoration using a neural network,” IEEE Trans. Acoust. Speech Signal Process. ASSP-36, 1141–1151 (1988). [CrossRef]
  10. K. Sivakumar, V. B. Desai, “Image Restoration using a multilayer perception with a multilevel sigmoidal function,” IEEE Trans. Acoust. Speech Signal Process. 41, 2018–2022 (1993). [CrossRef]
  11. W. Tai, R. Lin, C. Liou, “Blind deconvolution by self-organizing,” in Proceedings of the International Conference on Neural Networks (Institute of Electrical and Electronics Engineers, New York, 1997), pp. 1568–1573.
  12. S. Haykin, Neural Networks: a Comprehensive Foundation (Prentice-Hall, Englewood Cliffs, N.J., 1999).
  13. Y.-W. Chen, Z. Nakao, K. Arakaki, X. Fang, S. Tamura, “Restoration of gray images based on a genetic algorithm with Laplacian constraint,” Fuzzy Sets Syst. 103, 285–293 (1999). [CrossRef]
  14. R. Klette, P. Zamperon, Handbook of Image Processing Operators (Wiley, Chichester, UK, 1996).
  15. B. Gordon, R. Bender, G. T. Herman, “Algebraic reconstruction techniques (ART) for three-dimensional electron microscopy and X-ray photography,” J. Theor. Biol. 29, 471–481 (1970). [CrossRef] [PubMed]
  16. G. T. Herman, “Two direct methods for reconstructing pictures from their projections: a comparative study,” CVGIP Graph Models Image Process. 1, 123–144 (1972). [CrossRef]
  17. T. Kohonen, “The self-organizing map,” Proc. IEEE 78, 1464–1480 (1990). [CrossRef]
  18. Z. Michalewics, Genetic Algorithms + Data Structures = Evolution Programs (Springer-Verlag, Berlin, 1992). [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.

Figures

Fig. 1 Fig. 2 Fig. 3
 
Fig. 4 Fig. 5
 

« Previous Article  |  Next Article »

OSA is a member of CrossRef.

CrossCheck Deposited