Photon counting imaging based on GM-APD

Weiji, 何伟基 He; Bo-yu, 司马博羽 Sima; Yao-jin, 程耀进 Cheng; Wei, 成伟 Chen; Qian, 陈钱 Chen

doi:10.3788/ope.20122008.1831

Cited by 3 publications

(2 citation statements)

References 0 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The echo data of the aircraft model are derived from the literature. [33][34][35] To verify the convergence speed of the proposed algorithm, the existing algorithms [16][17][18][19] are first used to process the 1000-frame echo data of a single pixel, and the results are compared with those of the proposed algorithm. The single-frame runtime of each algorithm is presented in Table 1.…”

Section: Effect Of Pixel Fusion Of the Algorithm Convergence Speedmentioning

confidence: 99%

Adaptive fading Kalman depth estimation with chi-square hypothesis testing for Geiger-mode avalanche photo diode LIDAR

et al. 2023

View full text Add to dashboard Cite

.The operation of a Geiger-mode avalanche photo diode (GM-APD) LIDAR is severely disturbed by background noise, which makes it challenging to rapidly and accurately estimate the target depth. Therefore, we propose an adaptive fading Kalman depth estimation technique based on chi-square hypothesis testing for GM-APD LIDARs. First, by analyzing the consistency of the echo photon distribution between adjacent pixels of the GM-APD, the pixels are fused to rapidly obtain the echo data of the target surface, thereby accelerating the depth estimation process. Second, we design a chi-square hypothesis test condition based on the statistical characteristics of the innovation vector, which can help evaluate whether the fading factor is introduced at the current moment, promote the convergence of the algorithm, and reduce the depth estimation time. Third, we propose a fading memory index weighted method to adaptively adjust the weight of the observed values to accurately estimate the innovation matrix covariance and determine the optimal fading factor. We demonstrate the effectiveness of the proposed algorithm through simulations and experiments. The results show that the proposed algorithm can rapidly and accurately estimate the target depth in the presence of strong background noise.

show abstract

Section: Effect Of Pixel Fusion Of the Algorithm Convergence Speedmentioning

confidence: 99%

Adaptive fading Kalman depth estimation with chi-square hypothesis testing for Geiger-mode avalanche photo diode LIDAR

et al. 2023

View full text Add to dashboard Cite

show abstract

“…As a result of manufacturing difficulties and unstable performance, the resolution of APD arrays is still low [5,6,7]. An alternative method to obtain high resolution image is scanning imaging with a point detector such as Geiger-mode Avalanche Photodiode (APD) [8], photomultiplier tube (PMT) [9], and small-APD arrays detector. However, this method reduces the stability of the system and significantly increases imaging time due to low photon collection efficiency.…”

Section: Introductionmentioning

confidence: 99%

Adaptive Single Photon Compressed Imaging Based on Constructing a Smart Threshold Matrix

Shangguan

Yan

Wang

et al. 2018

Sensors

View full text Add to dashboard Cite

We demonstrate a single-photon compressed imaging system based on single photon counting technology and compressed sensing theory. In order to cut down the measurement times and shorten the imaging time, a fast and efficient adaptive sampling method, suited for single-photon compressed imaging, is proposed. First, the pre-measured rough images are transformed into sparse bases as a priori information. Then a smart threshold matrix is designed by using large sparse coefficients of the rough image in sparse bases. The adaptive measurement matrix is obtained by modifying the original Gaussian random matrix with the specially designed threshold matrix. Building the adaptive measurement matrix requires only one level of sparse representation, which means that adaptive imaging can be achieved quickly with very little computation. The experimental results show that the reconstruction effect of the image measured using the adaptive measurement matrix is obviously superior than that of the Gaussian random matrix under different measurement times and different reconstruction algorithms.

show abstract