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Machine Learning Technique Reconstructs Images Passing Through a Multimode Fiber


Approach could improve medical diagnostics, telecommunications

Through innovative use of a neural network that mimics image processing
by the human brain, a research team reports accurate reconstruction of
images transmitted over optical fibers for distances of up to a

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

"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."

Deciphering the blur

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

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 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 origin of neural networks, which is our very own
brain, the process is simple," explains Eirini Kakkava, a doctoral
student working on the project. "When a person stares at an object,
neurons in the brain are activated, indicating recognition of a familiar
object. Our brain can do this because it gets trained throughout our
life with images or signals of the same category of objects, which
changes the strength of the connections between the neurons." To train
an artificial neural network, researchers follow essentially the same
process, teaching the network to recognize certain images (in this case,
handwritten digits) until it is able to recognize images in the same
category as the training images that it has not seen before.

Learning by the numbers

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 percent
accuracy for images transmitted through a 0.1 meter long fiber and 90
percent accuracy with a 1 kilometer length of fiber.

A simpler method

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 and communications applications," Psaltis
said. Telecommunication signals often have to travel through many
kilometers of fiber and can suffer distortions, which this method could
correct. Doctors could use ultrathin fiber probes to collect images of
the tracts and arteries inside the human body without needing complex
holographic recorders or worrying about movement. "Slight movements
because of breathing or circulation can distort the images transmitted
through a multimode fiber," Psaltis said. 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.

Paper: N. Borhani, E. Kakkava, C. Moser, D. Psaltis, "Learning to
see through multimode fibers," Optica, volume 5, issue 8, pages
960-966 (2018).

About Optica

Optica is an open-access, online-only journal dedicated to the
rapid dissemination of high-impact peer-reviewed research across the
entire spectrum of optics and photonics. Published monthly by The
Optical Society (OSA), Optica provides a forum for pioneering
research to be swiftly accessed by the international community, whether
that research is theoretical or experimental, fundamental or applied. Optica
maintains a distinguished editorial board of more than 50 associate
editors from around the world and is overseen by Editor-in-Chief Alex
Gaeta, Columbia University, USA. For more information, visit Optica.

About The Optical Society

Founded in 1916, The Optical Society (OSA) is the leading professional
organization for scientists, engineers, students and business leaders
who fuel discoveries, shape real-life applications and accelerate
achievements in the science of light. Through world-renowned
publications, meetings and membership initiatives, OSA provides quality
research, inspired interactions and dedicated resources for its
extensive global network of optics and photonics experts. For more
information, visit

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