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
(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
Carlos A. Dávila and B. R. Hunt, "Superresolution of binary images with a nonlinear interpolative neural network," Appl. Opt. 39, 2291-2299 (2000)