Even with a mesh screen covering an object, (top), Stevens quantum 3D imaging technique that generates images 40,000x clearer (middle) than current technologies (bottom).
The technology is the first real-world demonstration of single-photon noise reduction using a method called Quantum Parametric Mode Sorting, or QPMS, which was first proposed by Huang and his team in a 2017 Nature paper. Unlike most noise-filtering tools, which rely on software-based post-processing to clean up noisy images, QPMS checks light's quantum signatures through exotic nonlinear optics to create an exponentially cleaner image at the level of the sensor itself.
Detecting a specific information-bearing photon amid the roar of background noise is like trying to pluck a single snowflake from a blizzard -- but that's exactly what Huang's team has managed to do. Huang and colleagues describe a method for imprinting specific quantum properties onto an outgoing pulse of laser light, and then filtering incoming light so that only photons with matching quantum properties are registered by the sensor.
The result: an imaging system that is incredibly sensitive to photons returning from its target, but that ignores virtually all unwanted noisy photons. The team's approach yields sharp 3D images even when every signal-carrying photon is drowned out by 34 times as many noisy photons.
"By cleaning up initial photon detection, we're pushing the limits of accurate 3D imaging in a noisy environment," said Patrick Rehain, a Stevens doctoral candidate and the study's lead author. "We've shown that we can reduce the amount of noise about 40,000 times better than the top current imaging technologies."
That hardware-based approach could facilitate the use of LIDAR in noisy settings where computationally intensive post-processing isn't possible. The technology could also be combined with software-based noise reduction to yield even better results. "We aren't trying to compete with computational approaches -- we're giving them new platforms to work in," Rehain said.