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Applied Optics

Applied Optics

APPLICATIONS-CENTERED RESEARCH IN OPTICS

  • Vol. 39, Iss. 14 — May. 10, 2000
  • pp: 2291–2299

Superresolution of binary images with a nonlinear interpolative neural network

Carlos A. Dávila and B. R. Hunt  »View Author Affiliations


Applied Optics, Vol. 39, Issue 14, pp. 2291-2299 (2000)
http://dx.doi.org/10.1364/AO.39.002291


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Abstract

Superresolution is the process by which the bandwidth of a diffraction-limited spectrum is extended beyond the optical passband. Many algorithms exist that are capable of superresolution; however, most are iterative methods, which are ill suited for real-time operation. One approach that has been virtually ignored is the neural-network approach. We consider the feedforward architecture known as a multilayer perceptron and present results on simulated binary images blurred by a diffraction-limited, circular-aperture optical transfer function and sampled at the Nyquist rate. To avoid aliasing, the network performs as a nonlinear spatial interpolator while simultaneously extrapolating in the frequency domain.

© 2000 Optical Society of America

OCIS Codes
(100.3010) Image processing : Image reconstruction techniques
(100.3020) Image processing : Image reconstruction-restoration
(100.6640) Image processing : Superresolution
(200.4260) Optics in computing : Neural networks

History
Original Manuscript: May 10, 1999
Revised Manuscript: September 21, 1999
Published: May 10, 2000

Citation
Carlos A. Dávila and B. R. Hunt, "Superresolution of binary images with a nonlinear interpolative neural network," Appl. Opt. 39, 2291-2299 (2000)
http://www.opticsinfobase.org/ao/abstract.cfm?URI=ao-39-14-2291


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