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A research team led by UCLA and the University of Rochester has demonstrated a promising evolution of an imaging system designed to capture details within "complex media," which scatter light, from depicting structures inside body tissue to seeing obstacles through heavy fog. The system uses physics-based machine learning to improve an existing imaging technique.
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In tests with standard calibration images obscured by complex media, the new system more than doubled the signal-to-noise ratio compared with a previous generation of the technology. The system also created images in near real time—thousandths of a second. The findings are published in the journal Light: Science & Applications.
Cheaper cameras, tougher conditions
Today, conventional applications for seeing inside complex media depend on expensive cameras that detect just beyond the limit of visible light, into the near-infrared.
In contrast, the underlying method that the researchers improved can use relatively inexpensive silicon-based cameras, like those found in smartphones. Introduced 10 years ago by study co-authors from the University of Rochester, the technique relies on a special film that lets through some photons and not others to convert scattered light from the near-infrared to the visible range.
However, this method tends to produce shadows in a vignetting effect that darkens the edges of images, reducing the field of view. Images can also include artifacts as lighter or darker splotches.
A physics check on reconstruction
The researchers merged the existing imaging technique with a machine learning framework called DeepTimeGate. It has two stages, starting with an algorithm trained to reconstruct images mathematically. The key addition is the second algorithm, developed at UCLA, which quickly performs a reality check, constraining results based on the fundamental rules of physics.
Sensing inside complex media in near real time using silicon-based cameras would be a boon for biomedical imaging. DeepTimeGate may lead to less expensive, more effective imaging to guide surgeries, including endoscopic procedures. Labs that test for dangerous microbes or anomalous cells in cloudy fluids such as blood could use a technology like this to analyze samples without diluting or filtering them.
Another potential application is in cameras for self-driving vehicles to detect their surroundings through blankets of rain, fog, dust or sand. In industry, the imaging system might one day help with quality control in manufacturing involving cloudy liquids or frosted packaging, as well as at waste removal plants.
More information: Hao Zhang et al, Hybrid deep reconstruction for vignetting-free upconversion imaging through scattering in epsilon-near-zero materials, Light: Science & Applications (2026). DOI: 10.1038/s41377-026-02375-6
Provided by University of California, Los Angeles
This story was originally published on Phys.org.
