title: Training of a neural network for image superresolution based on a nonlinear interpolative vector quantizer.
abstract: Superresolution is the process of extending the spectrum of a diffraction-limited image beyond the optical passband. We consider the neural-network approach to accomplish superresolution and present results on simulated gray-scale images degraded by diffraction blur and additive noise. Images are assumed to be sampled at the Nyquist rate, which requires spatial interpolation for avoiding aliasing, in addition to frequency-domain extrapolation. A novel, to our knowledge, use of vector quantization for the generation of training data sets is also presented. This is accomplished by training of a nonlinear vector quantizer, whose codebooks are subsequently used in the supervised training of the neural network with backpropagation.
authors: Department of Electrical and Computer Engineering, University of Arizona, Tucson, Arizona 85721, USA. cadavila@ece.arizona.edu
authors: C A
authors: CA
authors: Dávila
authors: B R
authors: BR
authors: Hunt