A University of Rochester team has developed a new imaging system that uses time-gating and near-infrared light to see through deep tissue, fog, and other obstacles, offering a cheaper alternative to specialized infrared cameras.
The University of Rochester’s breakthrough imaging technology, unveiled in two separate reports, uses a combination of near-infrared light, ultrafast optical “shuttering,” and machine learning to pierce through dense environments like biological tissue and fog. The system, which leverages inexpensive silicon detectors, could revolutionize medical imaging, autonomous vehicles, and other fields where traditional cameras struggle with scattering light.
How the Technology Works
The core of the innovation is a technique called time-gating, which functions like an ultrafast optical shutter. Instead of a mechanical shutter, the system uses precisely timed bursts of light to control when near-infrared photons are allowed to pass through the imaging pathway. This “optical gate” operates for about one picosecond—just enough time for light to travel a distance of the size of a period at the end of a sentence—allowing the system to isolate the earliest, most direct light signals while suppressing scattered photons.
In a traditional camera, the shutter is mechanical—when it opens, light comes in, and when it closes, light is rejected,
said Yang Xu, a University of Rochester doctoral researcher and lead author of the work. In this case, we use light to control light.
The system’s optical gate is a thin film of indium tin oxide, which converts near-infrared photons into visible light through a process called optical upconversion. This allows the use of standard silicon detectors, which are cheaper and more widely available than specialized near-infrared sensors.
The technology’s ability to reject scattered light addresses a major challenge in imaging. Near-infrared light, while less prone to scattering than visible light, still faces interference when passing through complex materials like tissue or fog. By isolating direct light paths, the system produces clearer images without the need for expensive equipment.
Expanding the Field of View with AI
In a separate study, researchers combined the time-gating method with artificial intelligence to significantly expand the system’s field of view. Working with collaborators at UCLA, the team used machine learning to reconstruct larger target areas, overcoming the limitations of the original technique. Before applying artificial intelligence, we could see only a limited field of view,
Xu said. By adding our collaborators’ methods, we can essentially reconstruct a much larger target area.
The AI-enhanced system could improve applications such as cancer detection, where broader imaging is critical, and LiDAR systems for autonomous vehicles, which often struggle in foggy or dusty conditions. Office of Naval Research, the National Science Foundation, and the Department of Energy.
The integration of AI not only broadens the system’s utility but also demonstrates the potential for combining optical techniques with machine learning to solve complex imaging challenges. Researchers at the University of Rochester’s lab of Robert Boyd, the William F. Krupke Distinguished Professor in Optics, have spent over a decade refining the time-gating approach, which is now being applied to real-world problems.
What’s Next for the Technology?
The technology is still in the research phase, but its potential applications are already drawing attention. Medical professionals could use it to improve diagnostics, while automotive engineers might integrate it into self-driving systems to enhance visibility in poor weather. The system’s reliance on affordable components also makes it a promising candidate for widespread adoption.
Researchers emphasize that the next steps involve testing the system in real-world scenarios and optimizing its performance. The team has already demonstrated its effectiveness in laboratory conditions, but practical implementation will require further development. As the technology evolves, it could redefine how imaging systems operate in challenging environments, from hospitals to highways.
The University of Rochester’s work highlights the growing intersection of optics, AI, and materials science. By leveraging existing technologies in novel ways, the team has created a system that is both innovative and accessible. As the research progresses, its impact on fields ranging from healthcare to transportation could be significant.
