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Proceedings Paper

Optical sensed image fusion with dynamic neural networks
Author(s): Yuri V. Shkvarko; Oscar G. Ibarra-Manzano; Rene Jaime-Rivas; Jose A. Andrade-Lucio; Edgar Alvarado-Mendez; R. Rojas-Laguna; Miguel Torres-Cisneros; J. A. Alvarez-Jaime
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Paper Abstract

The neural network-based technique for improving the quality of the image fusion is proposed as required for the remote sensing (RS) imagery. We prose to exit information about the point spread functions of the corresponding RS imaging systems combining it with prior realistic knowledge about the properties of the scene contained in the maximum entropy (ME) a priori image model. Applying the aggregate regularization method to solve the fusion tasks aimed to achieve the best resolution and noise suppression performances of the overall resulting image solves the problem. The proposed fusion method assumes the availability to control the design parameters, which influence the overall restoration performances. Computationally, the fusion method is implemented using the maximum entropy Hopfield-type neural network with adjustable parameters. Simulations illustrate the improved performances of the developed MENN-based image fusion method.

Paper Details

Date Published: 14 August 2001
PDF: 4 pages
Proc. SPIE 4419, 4th Iberoamerican Meeting on Optics and 7th Latin American Meeting on Optics, Lasers, and Their Applications, (14 August 2001); doi: 10.1117/12.437178
Show Author Affiliations
Yuri V. Shkvarko, Univ. de Guanajuato (Mexico)
Oscar G. Ibarra-Manzano, Univ. de Guanajuato (Mexico)
Rene Jaime-Rivas, Univ. de Guanajuato (Mexico)
Jose A. Andrade-Lucio, Univ. de Guanajuato (Mexico)
Edgar Alvarado-Mendez, Univ. de Guanajuato (Mexico)
R. Rojas-Laguna, Univ. de Guanajuato (Mexico)
Miguel Torres-Cisneros, Univ. de Guanajuato (Mexico)
J. A. Alvarez-Jaime, Univ. de Guanajuato (Mexico)


Published in SPIE Proceedings Vol. 4419:
4th Iberoamerican Meeting on Optics and 7th Latin American Meeting on Optics, Lasers, and Their Applications

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