Machine learning technique rebuilds images passing through multimode fiber

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 kilometer, the researchers said.

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.

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.

To tackle this problem, researchers 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 percent accuracy for images transmitted through a 0.1 meter long fiber and 90 percent accuracy with a 1 kilometer length of fiber.

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.

Founded in 1916, The Optical Society (OSA) is the 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 osa.org.