Training of a Neural Network for Image Superresolution Based on a Nonlinear Interpolative Vector Quantizer
Applied Optics, Vol. 39, Issue 20, pp. 3473-3485 (2000)
http://dx.doi.org/10.1364/AO.39.003473
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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.
© 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
Citation
Carlos A. Dávila and B. R. Hunt, "Training of a Neural Network for Image Superresolution Based on a Nonlinear Interpolative Vector Quantizer," Appl. Opt. 39, 3473-3485 (2000)
http://www.opticsinfobase.org/ao/abstract.cfm?URI=ao-39-20-3473
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