Researchers at the University of Rochester have developed a lower-cost imaging system that can produce clear pictures through dense fog, deep tissue, and other materials that scatter light. The technology was outlined in a paper published in Nature Communications, according to a report by Phys.org.
Most modern imaging systems that need to see through difficult environments use near-infrared light. Near-infrared works better than visible light in many conditions, but it still breaks down when photons scatter through materials like biological tissue or thick fog. On top of that, the specialized detectors that near-infrared systems typically require are made from expensive materials, limiting how widely the technology can be used.
The Rochester system gets around both problems. It uses inexpensive silicon-based detectors and converts near-infrared light into visible light in real time, producing clearer images at lower cost.
The method is called time-gating, a technique the laboratory of Robert Boyd, the William F. Krupke Distinguished Professor in Optics, has spent more than a decade developing.
"Time-gating essentially works like the shutter in a camera," said Yang Xu, the lead author of the paper and a doctoral graduate of the university. "In a traditional camera, the shutter is mechanical — when it opens, light comes in, and when it closes, light is rejected. In this case, we use light to control light."
The shutter in this system is not mechanical. Instead, ultrafast bursts of light open and close a gate made of a thin film of indium tin oxide. The gate stays open for only about one picosecond, which is approximately the time it takes light to travel a distance the size of a period at the end of a sentence. Any near-infrared photons that pass through are converted to visible light for an immediate, clear image.
The researchers identified several practical applications for the technology. In medicine, it could improve imaging for cancer detection. In transportation, it could sharpen the performance of LiDAR systems used in self-driving cars, which struggle in foggy or other low-visibility conditions.
The team also extended the system's capabilities by partnering with researchers at UCLA to add machine learning. That work was published separately in the journal Light: Science and Applications.
"Before applying artificial intelligence, we could see only a limited field of view," said Xu. "By adding our collaborators' methods, we can essentially reconstruct a much larger target area, enlarging the field of view our ultrafast time-gating technique can capture."
Other University of Rochester collaborators on the studies included optics alumna Saumya Choudhary and a physics doctoral researcher. The Boyd laboratory has been refining time-gating for more than a decade, and the addition of machine learning represents the latest step in expanding what the system can do.
