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Nature Communications

Noise-tolerant single photon sensitive three-dimensional imager

Patrick Rehain, Yong Meng Sua, Shenyu Zhu, Ivan Dickson, Bharathwaj Muthuswamy, Jeevanandha Ramanathan, Amin Shahverdi, Yu‐Ping Huang

Published Feb 17, 2020• 83 citations
Publisher

Abstract

Active imagers capable of reconstructing 3-dimensional (3D) scenes in the presence of strong background noise are highly desirable for many sensing and imaging applications. A key to this capability is the time-resolving photon detection that distinguishes true signal photons from the noise. To this end, quantum parametric mode sorting (QPMS) can achieve signal to noise exceeding by far what is possible with typical linear optics filters, with outstanding performance in isolating temporally and spectrally overlapping noise. Here, we report a QPMS-based 3D imager with exceptional detection sensitivity and noise tolerance. With only 0.0006 detected signal photons per pulse, we reliably reconstruct the 3D profile of an obscured scene, despite 34-fold spectral-temporally overlapping noise photons, within the 6 ps detection window (amounting to 113,000 times noise per 20 ns detection period). Our results highlight a viable approach to suppress background noise and measurement errors of single photon imager operation in high-noise environments.

DOI
10.1038/s41467-020-14591-8

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Active imagers capable of reconstructing 3-dimensional (3D) scenes in the presence of strong background noise are highly desirable for many sensing and imaging applications. A key to this capability is the time-resolving photon detection that distinguishes true signal photons from the noise. To this end, quantum parametric mode sorting (QPMS) can achieve signal to noise exceeding by far what is possible with typical linear optics filters, with outstanding performance in isolating temporally and spectrally overlapping noise. Here, we report a QPMS-based 3D imager with exceptional detection sensitivity and noise tolerance. With only 0.0006 detected signal photons per pulse, we reliably reconstruct the 3D profile of an obscured scene, despite 34-fold spectral-temporally overlapping noise photons, within the 6 ps detection window (amounting to 113,000 times noise per 20 ns detection period). Our results highlight a viable approach to suppress background noise and measurement errors of single photon imager operation in high-noise environments.
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 (∼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.
Selective detection of signal over noise is essential to measurement and signal processing. Time-frequency filtering has been the standard approach for the optimal detection of non-stationary signals. However, there is a fundamental tradeoff between the signal detection efficiency and the amount of undesirable noise detected simultaneously, which restricts its uses under weak signal yet strong noise conditions. Here, we demonstrate quantum parametric mode sorting based on nonlinear optics at the edge of phase matching to improve the tradeoff. By tailoring the nonlinear process in a commercial lithium-niobate waveguide through optical arbitrary waveform generation, we demonstrate highly selective detection of picosecond signals overlapping temporally and spectrally but in orthogonal time-frequency modes as well as against broadband noise, with performance well exceeding the theoretical limit of the optimized time-frequency filtering. We also verify that our device does not introduce any significant quantum noise to the detected signal and demonstrate faithful detection of pico-second single photons. Together, these results point to unexplored opportunities in measurement and signal processing under challenging conditions, such as photon-starving quantum applications.
Detection signal to noise ratio lies at the heart of any remote sensing task. Matched filters have long been deployed as the optimal devices for radio and microwave receiving. However, their implements in the optics domain are challenging due to a much higher bandwidth and the intensity-only measurement by incoherent detectors. For applications involving high noise and low signal, their utilities are overly restricted by a fundamental trade-off between the signal detection efficiency and noise rejection. In this talk, I will describe a nascent approach, quantum parametric mode sorting, to addressing these practical and fundamental challenges. It is realized via quantum frequency conversion at the phase matching edge, where the signal distillation, picosecond timing tagging, and wavelength transduction are accomplished during a single pass through a nonlinear waveguide or crystal. I will report our experimental progress on using it a variety of single photon sensing and imaging tasks.
Quantum-inspired imaging techniques have been proven to be effective for LiDAR with the advances of single photon detectors and computational algorithms. However, due to the disturbance of background noise and the varies of signal in outdoor environment, the performance of LiDAR is still far from its ultimate limit set by the quantum fluctuations of coherent probe light. In this work, we propose and demonstrate a LiDAR from the detection perspective for approaching the standard quantum-limited performance. The photon numbers of echo signals are recorded by a photon-number-resolving detector and applied to overcome heavy background noise through an active photon number filter in the LiDAR. It can approach the standard quantum limit in intensity estimation in a wide photon-flux range, and achieve a Fisher information of only 0.04 dB less than the quantum Fisher information when the mean signal photon number is 10. Experimentally, a noise-free target reconstruction and imaging is demonstrated in the daytime by the proposed LiDAR. It also performs better in reflectivity resolution when taking only 1/1000 of the measurements based on on/off detection. This work provides a fundamental strategy for constructing a LiDAR to quickly extract targets and identify materials in complex environments, which is important for intelligent agents such as autonomous vehicles.
Abstract Imaging, detection and ranging of objects in the presence of significant background noise is a fundamental challenge in optical sensing. Overcoming the limitations imposed in conventional methods, quantum light sources show higher resistance against noise in a time-correlation-based quantum illumination. Here, we introduce the advantage of using not only time correlations but also polarization correlations in photon pairs in the detection of an object that is embedded in a noisy background. In this direction, a time- and polarization-correlated photon pair source using the spontaneous parametric down-conversion process is exploited. We found that the joint measurement of correlated pairs allows distinguishing the signal from the noise photons and that leads to an improved signal-to-noise ratio. Our comparative study revealed that using polarization correlations in addition to time correlations provides improved noise rejection. Furthermore, we show that polarization correlation allows undoing the detector limitation where high background often leads to detector saturation.
To capture a three-dimensional (3D) image, conventional methods often require multiple two-dimensional images of the scene from different perspectives. Here we show the reconstruction of a 3D scene with a fixed camera using quantum correlated photon pairs from a single source. Correlated photon pairs are effectively mirror images of each other so, by measuring the emission position of one photon, and the emission angle of the other, the entire propagation trajectory of the pair, and, subsequently, the 3D scene, can be reconstructed. The high photon correlation and low photon flux from a quantum source also makes it well suited for 3D imaging of light sensitive samples.