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1. Motion Adaptive Deblurring With Single-Photon Cameras

   Seets, Trevor; Ingle, Atul; Laurenzis, Martin; Velten, Andreas (January 2021, Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision (WACV))

   null (Ed.)

   Single-photon avalanche diodes (SPADs) are a rapidly developing image sensing technology with extreme low-light sensitivity and picosecond timing resolution. These unique capabilities have enabled SPADs to be used in applications like LiDAR, non-line-of-sight imaging and fluorescence microscopy that require imaging in photon-starved scenarios. In this work we harness these capabilities for dealing with motion blur in a passive imaging setting in low illumination conditions. Our key insight is that the data captured by a SPAD array camera can be represented as a 3D spatio-temporal tensor of photon detection events which can be integrated along arbitrary spatio-temporal trajectories with dynamically varying integration windows, depending on scene motion. We propose an algorithm that estimates pixel motion from photon timestamp data and dynamically adapts the integration windows to minimize motion blur. Our simulation results show the applicability of this algorithm to a variety of motion profiles including translation, rotation and local object motion. We also demonstrate the real-world feasibility of our method on data captured using a 32x32 SPAD camera. 

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2. Time-magnified photon counting with 550-fs resolution

   https://doi.org/10.1364/OPTICA.420816  

   Li, Bowen; Bartos, Jan; Xie, Yijun; Huang, Shu-Wei (August 2021, Optica)

   Time-correlated single-photon counting (TCSPC) is an enabling technology for applications such as low-light fluorescence lifetime microscopy and photon counting time-of-flight (ToF) 3D imaging. However, state-of-the-art TCSPC single-photon timing resolution (SPTR) is limited to 3–100 ps by single-photon detectors. Here, we experimentally demonstrate a time-magnified TCSPC (TM-TCSPC) that achieves an ultrashort SPTR of 550 fs with an off-the-shelf single-photon detector. The TM-TCSPC can resolve ultrashort pulses with a 130-fs pulse width difference at a 22-fs accuracy. When applied to photon counting ToF 3D imaging, the TM-TCSPC greatly suppresses the range walk error that limits all photon counting ToF 3D imaging systems by 99.2% and thus provides high depth accuracy and precision of 26 µm and 3 µm, respectively. 

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3. Dithered depth imaging

   https://doi.org/10.1364/OE.408800  

   Rapp, Joshua; Dawson, Robin_M_A; Goyal, Vivek_K (November 2020, Optics Express)

   Single-photon lidar (SPL) is a promising technology for depth measurement at long range or from weak reflectors because of the sensitivity to extremely low light levels. However, constraints on the timing resolution of existing arrays of single-photon avalanche diode (SPAD) detectors limit the precision of resulting depth estimates. In this work, we describe an implementation of subtractively-dithered SPL that can recover high-resolution depth estimates despite the coarse resolution of the detector. Subtractively-dithered measurement is achieved by adding programmable delays into the photon timing circuitry that introduce relative time shifts between the illumination and detection that are shorter than the time bin duration. Careful modeling of the temporal instrument response function leads to an estimator that outperforms the sample mean and results in depth estimates with up to 13 times lower root mean-squared error than if dither were not used. The simple implementation and estimation suggest that globally dithered SPAD arrays could be used for high spatial- and temporal-resolution depth sensing. 

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4. Single-Photon Camera Guided Extreme Dynamic Range Imaging

   Liu, Yuhao; Gutierrez-Barragan, Felipe; Ingle, Atul; Gupta, Mohit; Velten, Andreas (January 2022, Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision (WACV))

   Reconstruction of high-resolution extreme dynamic range images from a small number of low dynamic range (LDR) images is crucial for many computer vision applications. Current high dynamic range (HDR) cameras based on CMOS image sensor technology rely on multiexposure bracketing which suffers from motion artifacts and signal-to-noise (SNR) dip artifacts in extreme dynamic range scenes. Recently, single-photon cameras (SPCs) have been shown to achieve orders of magnitude higher dynamic range for passive imaging than conventional CMOS sensors. SPCs are becoming increasingly available commercially, even in some consumer devices. Unfortunately, current SPCs suffer from low spatial resolution. To overcome the limitations of CMOS and SPC sensors, we propose a learning-based CMOS-SPC fusion method to recover high-resolution extreme dynamic range images. We compare the performance of our method against various traditional and state-of-the-art baselines using both synthetic and experimental data. Our method outperforms these baselines, both in terms of visual quality and quantitative metrics. 

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5. Super-Resolution Imaging by Computationally Fusing Quantum and Classical Optical Information

   https://doi.org/10.34133/icomputing.0003  

   Bartels, Randy A.; Murray, Gabe; Field, Jeff; Squier, Jeff (January 2022, Intelligent Computing)

   A high-speed super-resolution computational imaging technique is introduced on the basis of classical and quantum correlation functions obtained from photon counts collected from quantum emitters illuminated by spatiotemporally structured illumination. The structured illumination is delocalized—allowing the selective excitation of separate groups of emitters as the modulation of the illumination light advances. A recorded set of photon counts contains rich quantum and classical information. By processing photon counts, multiple orders of Glauber correlation functions are extracted. Combinations of the normalized Glauber correlation functions convert photon counts into signals of increasing order that contain increasing spatial frequency information. However, the amount of information above the noise floor drops at higher correlation orders, causing a loss of accessible information in the finer spatial frequency content that is contained in the higher-order signals. We demonstrate an efficient and robust computational imaging algorithm to fuse the spatial frequencies from the low-spatial-frequency range that is available in the classical information with the spatial frequency content in the quantum signals. Because of the overlap of low spatial frequency information, the higher signal-to-noise ratio (SNR) information concentrated in the low spatial frequencies stabilizes the lower SNR at higher spatial frequencies in the higher-order quantum signals. Robust performance of this joint fusion of classical and quantum computational single-pixel imaging is demonstrated with marked increases in spatial frequency content, leading to super-resolution imaging, along with much better mean squared errors in the reconstructed images. 

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