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Abstract
The quality of an image is limited to the signal-to-noise ratio of the output from sensors. As the background noise increases much more than the signal, which can be caused by either a huge attenuation of light pulses after a long-haul transmission or a blinding attack with a strong flood illumination, an imaging system stops working properly. Here we built a superconducting single-photon infrared camera of negligible dark counts and 60 ps timing resolution. Combining with an adaptive 3D slicing algorithm that gives each pixel an optimal temporal window to distinguish clustered signal photons from a uniformly distributed background, we successfully reconstructed 3D single-photon images at both a low signal level (${\sim}\;{{1}}$ average photon per pixel) and extremely high noise background (background-to-signal ratio = 200 within a period of 50 ns before denoising). Among all detection events, we were able to remove 99.45% of the noise photons while keeping the signal photon loss at 0.74%. This Letter is a direct outcome of quantum-inspired imaging that asks for a co-development of sensors and computational methods. We envision that the proposed methods can increase the working distance of a long-haul imaging system or defend it from blinding attacks.
© 2020 Optical Society of America
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