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

Training of a neural network for image superresolution based on a nonlinear interpolative vector quantizer

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