This paper, written for interdisciplinary audience, presents computational image reconstruction implementable by quantum optics. The input-triggered selection of a high-resolution image among many stored ones, and its reconstruction if the input is occluded or noisy, has been successfully simulated. The original algorithm, based on the Hopfield associative neural net, was transformed in order to enable its quantum-wave implementation based on holography. The main limitations of the classical Hopfield net are much reduced with the simulated new quantum-optical implementation.
© 2004 Optical Society of America
(090.0090) Holography : Holography
(100.3010) Image processing : Image reconstruction techniques
(200.4490) Optics in computing : Optical buffers
(200.4700) Optics in computing : Optical neural systems
(270.0270) Quantum optics : Quantum optics
Mitja Peruš, Horst Bischof, H. John Caulfield, and Chu Kiong Loo, "Quantum-Implementable Selective Reconstruction of High-Resolution Images," Appl. Opt. 43, 6134-6138 (2004)