This article has been reviewed according to Science X's editorial process and policies. Editors have highlighted the following attributes while ensuring the content's credibility: From helping doctors detect cancer to guiding self-driving cars through traffic, many modern imaging systems rely on near-infrared light, producing a crisp picture when visible light would scatter and yield a blurry picture. But near-infrared systems struggle when light passes through materials like deep tissue or dense fog, succumbing to the same scattering effect in which photons deviate from their path.
Existing near-infrared imaging systems also rely on specialized detectors made from expensive materials, limiting their affordability and widespread use. University of Rochester researchers have now developed a lower-cost imaging system that overcomes both challenges. Using inexpensive silicon-based detectors, the system quickly converts near-infrared light to visible light while producing clearer images through these difficult environments.
The technology, outlined in a recent paper published in Nature Communications, uses a technique called time-gating that the laboratory of Robert Boyd, the William F. Krupke Distinguished Professor in Optics, has spent more than a decade refining. "Time-gating essentially works like the shutter in a camera," says Yang Xu '26 (Ph.D.), the lead author of the paper.
"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." Ultrafast bursts of light act as the shutter, letting infrared photons through the gate for only about a picosecond. For reference, a picosecond is the time it takes light to travel a distance roughly the size of a period at the end of a sentence.
The gate is a thin film made of indium tin oxide, and any near-infrared photons that hit it are converted to visible light for a clear picture in real time. The approach could improve image quality for applications ranging from biomedical imaging for cancer detection to LiDAR (light detection and ranging) systems used in autonomous vehicles, where fog and other light-scattering conditions can limit performance. While the time-gating technique produced remarkably clear images, Boyd, Xu and their colleagues found a way to make the system even more useful.
Working with researchers at UCLA, they combined their approach with machine learning to dramatically expand the system's field of view. Their findings were published in a recent paper in Light: Science and Applications. "Before applying artificial intelligence, we could see only a limited field of view," says Xu.
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