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In The Optical Society's journal for high-impact research, Optica, the researchers report teaching a type of machine learning algorithm known as a deep neural network to recognize images of numbers from the pattern of speckles they create when transmitted to the far end of a fiber. 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.
"We use modern deep neural network architectures to retrieve the input images from the scrambled output of the fiber," said Demetri Psaltis, Swiss Federal Institute of Technology, Lausanne, who led the research in collaboration with colleague Christophe Moser. "We demonstrate that this is possible for fibers up to 1 kilometer long" he added, calling the work "an important milestone."
Fibers - Information - Light - Multimode - Fibers
Optical fibers transmit information with light. Multimode fibers have much greater information-carrying capacity than single-mode fibers. Their many channels -- known as spatial modes because they have different spatial shapes -- can transmit different streams of information simultaneously.
While multimode fibers are well suited for carrying light-based signals, transmitting images is problematic. Light from the image travels through all of the channels and what comes out the other end is a pattern of speckles that the human eye cannot decode.
Problem - Psaltis - Team - Network - Type
To tackle this problem, Psaltis and his team turned to a deep neural network, a type of machine learning algorithm that functions much the way the brain does. Deep neural networks can give computers the ability to identify objects in photographs and help improve speech recognition systems. Input is processed through several layers of artificial neurons, each of which performs a small calculation and passes the result on to the next layer. The machine learns to identify the input by recognizing the patterns of output associated with it.
"If we think about the...
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