In the latest issue of The Optical Society’s journal, Optica, researchers in Switzerland 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,” explains Demetri Psaltis of the Swiss Federal Institute of Technology in Lausanne, Switzerland, 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.”
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. But 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.
To tackle that 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.
To train their system, the researchers turned to a database containing 20,000 samples of handwritten numbers, 0 through 9. They selected 16,000 to be used as training data and kept aside 2,000 to validate the training and another 2,000 for testing the validated system. They used a laser to illuminate each digit and sent the light beam through an optical fiber, which had approximately 4,500 channels, to a camera on the far end. A computer measured how the intensity of the output light varied across the captured image, and they collected a series of examples for each digit. Although the speckle patterns collected for each digit looked the same to the human eye, the neural network was able to discern differences and recognize patterns of intensity associated with each digit. Testing with the set-aside images showed that the algorithm achieved 97.6% accuracy for images transmitted through a 0.1-meter-long fiber and 90% accuracy with a 1-kilometer fiber.
Navid Borhani, a research team member, says this machine learning approach is much simpler than other methods to reconstruct images passed through optical fibers, which require making a holographic measurement of the output. The neural network was also able to cope with distortions caused by environmental disturbances to the fiber such as temperature fluctuations or movements caused by air currents that can add noise to the image—a situation that gets worse with fiber length.
“The remarkable ability of deep neural networks to retrieve information transmitted through multimode fibers is expected to benefit medical procedures like endoscopy…” Psaltis says, adding that doctors could use ultrathin fiber probes to collect images of the tracts and arteries inside the human body without needing complex holo-graphic recorders or worrying about movement. “Slight movements because of breathing or circulation can distort the images transmitted through a multimode fiber, The deep neural networks are a promising solution for dealing with that noise.”
Psaltis and his team plan to try the technique with biological samples, to see if that works as well as reading handwritten numbers. They hope to conduct a series of studies using different categories of images to explore the possibilities and limits of their technique.