Fast Long-Range Photon Counting Depth Imaging With Sparse Single-Photon Data

Kang, Yan; Li, Lifei; Liu, Dawei; Li, Miaosi; Zhang, Tong‐Yi; Zhao, Wei

doi:10.1109/jphot.2018.2840681

Cited by 35 publications

(12 citation statements)

References 32 publications

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“…Along with qualitative measures, a series of quantitative measures are also proposed within the less common metrics equations given as follows. The restoration quality was evaluated using the reconstruction signal-to-noise ratio (RSNR), 15,16 which is a metric that has been used for depth image reconstruction on sparse single photon data.…”

Section: Methodsmentioning

confidence: 99%

Imaging from temporal data via spiking convolutional neural networks

Kirkland

Kapitany

Lyons

et al. 2020

Emerging Imaging and Sensing Technologies for Security and Defence V; And Advanced Manufacturing Technologies for Micro- And Na

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A new approach for imaging that is solely based on the time of flight of photons coming from the entire imaged scene, combined with a novel machine learning algorithm for image reconstruction: a spiking convolutional neural network (SCNN) named Spike-SPI (Spiking-Single Pixel Imager). The approach uses a single point detector and the corresponding time-counting electronics, which provide the arrival time of photons in the form of spikes distributed over time. This data is transformed into a temporal histogram containing the number of photons per arrival time. A SCNN that converts the 1D temporal histograms into a 3D image (2D image with depth map) by exploiting the feature extraction capabilities of convolutional neural networks (CNNs), the high dimensional compressed latent space representations of a variational encoder-decoder network structure, and the asynchronous processing capabilities of a spiking neural network (SNN). The performance of the proposed SCNN is analysed to demonstrate the state-of-the-art feature extraction capabilities of CNNs and the low latency asynchronous processing of SNNs that offer both higher throughput and higher accuracy in image reconstruction from the ToF data, when compared to standard ANNs. The results of Spike-SPI show an increase in spatial accuracy of 15% over then ANN, using the Intersection of Union (IoU) for the objects in the scene. While also delivering a 100% increase over then ANN in object reconstruction signal to noise ratio (RSNR) from ∼3dB to ∼6dB. These results are also consistent across a range of IRF (Instrument Response Functions) values and photo counts, highlighting the robust nature of the new network structure. Moreover, the asynchronous processing nature of the spiking neurons allow for a faster throughput and less computational overhead, benefiting from the operational sparsity in the single point sensor.

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Section: Methodsmentioning

confidence: 99%

Imaging from temporal data via spiking convolutional neural networks

Kirkland

Kapitany

Lyons

et al. 2020

Emerging Imaging and Sensing Technologies for Security and Defence V; And Advanced Manufacturing Technologies for Micro- And Na

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“…TCSPC was mainly used in the detection of fluorescence lifetime in the early stage [4][5][6][7][8][9][10][11][12]. After that, TCSPC began to be used in the field of laser ranging [13][14][15][16][17][18] and gradually developed to the direction of laser long-distance 3D imaging based on time-of-flight (TOF) algorithm [19][20][21][22][23][24][25][26]. Recently, Li et al achieved the imaging of targets 45 kilometers away [27].…”

Section: Introductionmentioning

confidence: 99%

Three-Dimensional Imaging via Time-Correlated Single-Photon Counting

Zheng

Gao

et al. 2020

Applied Sciences

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Three-dimensional (3D) imaging under the condition of weak light and low signal-to-noise ratio is a challenging task. In this paper, a 3D imaging scheme based on time-correlated single-photon counting technology is proposed and demonstrated. The 3D imaging scheme, which is composed of a pulsed laser, a scanning mirror, single-photon detectors, and a time-correlated single-photon counting module, employs time-correlated single-photon counting technology for 3D LiDAR (Light Detection and Ranging). Aided by the range-gated technology, experiments show that the proposed scheme can image the object when the signal-to-noise ratio is decreased to −13 dB and improve the structural similarity index of imaging results by 10 times. Then we prove the proposed scheme can image the object in three dimensions with a lateral imaging resolution of 512 × 512 and an axial resolution of 4.2 mm in 6.7 s. At last, a high-resolution 3D reconstruction of an object is also achieved by using the photometric stereo algorithm.

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“…Photon counting lidar has attracted amount of attentions for three-dimensional (3D) imaging for many applications in recent years [1]- [5].Geiger-mode avalanche photodiode (Gm-APD) is widely used in the 3-D imaging lidar system to detect weak signals because of their single-photon sensitivity and picosecond magnitude time precision [6]- [9]. A photon-counting lidar measures the flight time of laser signals by utilizing pulses as short as picoseconds to achieve high-precision ranging [10]- [12].…”

Section: Introductionmentioning

confidence: 99%

Dual Gm-APD Polarization Lidar to Acquire the Depth Image of Shallow Semitransparent Media With a Wide Laser Pulse

Yang

et al. 2020

IEEE Photonics J.

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A polarization lidar with dual Geiger-mode avalanche photodiodes (Gm-APDs) is designed to obtain the high-precision depth image of shallow semitransparent media by using a wide laser pulse. The first surface of the semitransparent media is smooth and can nearly preserve the incident polarization, whereas the second surface is rough and can depolarize the incident polarization. A polarization splitting prism is utilized to separate the two echo signals, which will be detected by dual Gm-APDs. It gives the ability to obtain the extremely shallow depth independent of laser pulse width and dead time of Gm-APD. The theory of laser polarization transmission of dual Gm-APD polarization lidar is analyzed. Signal restoration & center-of-mass algorithm method is used to restrain the range walk error and obtain high range precision. The depth image of shallow semitransparent media with a thickness and distance of 10 cm and 6 m, respectively, is obtained in the experiment with a range precision of 1.1 cm. An experiment of real shallow water layer with different depth features is also performed to verify the result.

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Fast Long-Range Photon Counting Depth Imaging With Sparse Single-Photon Data

Cited by 35 publications

References 32 publications

Imaging from temporal data via spiking convolutional neural networks

Imaging from temporal data via spiking convolutional neural networks

Three-Dimensional Imaging via Time-Correlated Single-Photon Counting

Dual Gm-APD Polarization Lidar to Acquire the Depth Image of Shallow Semitransparent Media With a Wide Laser Pulse

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