Science Daily August 9, 2018
Researchers in Switzerland used deep neural networks (DNNs) to classify and reconstruct the input images from the intensity of the speckle patterns that result after the inputs are propagated through multimode fiber. They demonstrated this result for fibers up to 1 km long by training the DNNs with a database of 16,000 handwritten digits. Better recognition accuracy was obtained when the DNNs were trained to first reconstruct the input and then classify based on the recovered image. They reported remarkable robustness against environmental instabilities and tolerance to deviations of the input pattern from the patterns with which the DNN was originally trained. The work could improve endoscopic imaging for medical diagnosis, boost the amount of information carried over fiber-optic telecommunication networks, or increase the optical power delivered by fibers… read more. Open Access TECHNICAL ARTICLEÂ
A speckle pattern from an image transmitted through a multimode fiber passes through the hidden layers of a deep neural network and is reproduced as the number 3. Credit: Demetri Psaltis, Swiss Federal Institute of Technology Lausanne