UCLA RESEARCH BRIEF FINDINGS 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 upon an existing imaging technique. In tests with standard calibration images obscured by complex media, the new system more than doubled the signal-to-noise ratio compared to a previous generation of the technology. The system also created images in close to real time — thousandths of a second. BACKGROUND 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 cheap 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. And images can include artifacts as lighter or darker splotches. METHOD 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. IMPACT 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,