A photon counting 3D imaging system with short-pulsed structured illumination and a
single-pixel photon counting detector is built. The proposed multiresolution photon counting 3D
imaging technique acquires a high-resolution 3D image from a coarse image and details at successfully
finer resolution sampled by Hadamard multiplexing along with the wavelet trees. The detected power is
significant increased thanks to the Hadamard multiplexing. Both the required measurements and the
reconstruction time can be significant reduced, which makes the proposed technique suitable for scenes
with high spatial resolution. Since the depth map is retrieved through a linear inverse Hadamard
transform instead of the computational intensive optimization problems performed in CS, the time
consumed to retrieve the depth map can be also reduced, and thus it will be suitable for applications of
real-time compressed 3D imaging such as object tracking. Even though the resolution of the final 3D
image can be high, the number of measurements remains small due to the adaptivity of the
wavelet-trees-based sampling strategy. The adaptive sampling technique is quality oriented, allowing
more control over the image quality. The experimental results indicate that both the intensity image and
depth map of a scene at resolutions up to 512×512 pixels can be acquired and retrieved with practical
times as low as 17 seconds.
Computational ghost imaging (CGI) is mainly used to reconstruct grayscale images at present and there are few researches aiming at color images. In this paper, we both theoretically and experimentally demonstrate a colored adaptive compressed imaging method. Benefiting from imaging in YUV color space, the proposed method adequately exploits the sparsity of U, V components in the wavelet domain, the interdependence between luminance and chrominance, and the human visual characteristics. The simulation and experimental results show that our method greatly reduces the measurements required, and offers better image quality compared to recovering red (R), green (G) and blue (B) components separately in RGB color space. As the application of single photodiode increases, our method shows great potential in many fields.
Proc. SPIE. 9279, Real-time Photonic Measurements, Data Management, and Processing
KEYWORDS: Signal to noise ratio, Photon counting, Interference (communication), Time correlated photon counting, Monte Carlo methods, Signal detection, Probability theory, Statistical modeling, Photon transport, Ranging
This paper investigates the random bitstream ranging model and proposes a new output SNR model based on statistical optics theory. We study the relationship of SNR and the fraction of 1 in bitstream with different dead time by Monte Carlo simulation. Theory model is almost consistent with Monte Carlo simulation. The results show that with the fraction of randomly distributed 1-bits in transmitted pattern increased, the system SNR gets better first and then gets worse. Best pattern of transmitted bit stream according to different dead time leads to the best SNR. According to new output SNR model, low dead time brings better SNR. The system SNR increases firstly then gets down with the growing signal photon counts. At last, Gaussian distribution timing jitter of 440ps FWHM is introduced to reconstruct received bitstream pattern formed from the arrival times of returning single photon. We find that higher rate of bitstream brings higher possibility error of single time value. Suitable bits rate is restricted to 1 GHz according to jitter of 440ps FWHM to reduce the probability of ranging error.
Due to the numerous applications employed 3D data such as target detection and recognition, three-dimensional (3D) active imaging draws great interest recently. Employing a pulsed laser as the illumination source and an intensified sensor as the image sensor, the 3D active imaging method emits and then records laser pulses to infer the distance between the target and the sensor. One of the limitations of the 3D active imaging is that acquiring depth map with high depth resolution requires a full range sweep, as well as a large number of detections, which limits the detection speed. In this work, a compressed gating method combining the 3D active imaging and compressive sensing (CS) is proposed on the basis of the random gating method to achieve the depth map reconstruction from a significantly reduced number of detections. Employing random sequences to control the sensor gate, this method estimates the distance and reconstructs the depth map in the framework of CS. A simulation was carried out to estimate the performance of the proposed method. A scene generated by the 3ds Max was employed as target and a reconstruction algorithm was used to recover the depth map in the simulation. The simulation results have shown that the proposed method can reconstruct the depth map with slight reconstruction error using as low as 7% detections that the conventional method requires and achieve perfect reconstruction from about 10% detections under the same depth resolution. It has also indicated that the number of detections required is affected by depth resolution, noise generated by a variety of reasons and complexity of the target scene. According to the simulation results, the compressed gating method is able to be used in the case of long range with high depth resolution and robust to various types of noise. In addition, the method is able to be used for multiple-return signals measurement without increase in the number of detections.
A real-time correction method for range walk error in photon counting 3D imaging Lidar is proposed in this paper. We establish the photon detection model and pulse output delay model for GmAPD, which indicates that range walk error in photon counting 3D imaging Lidar is mainly effected by the number of photons during laser echo pulse. A measurable variable – laser pulse response rate is defined as a substitute of the number of photons during laser echo pulse, and the expression of the range walk error with respect to the laser pulse response rate is obtained using priori calibration. By recording photon arrival time distribution, the measurement error of unknown targets is predicted using established range walk error function and the range walk error compensated image is got. Thus real-time correction of the measurement error in photon counting 3D imaging Lidar is implemented. The experimental results show that the range walks error caused by the difference in reflected energy of the target can be effectively avoided without increasing the complexity of photon counting 3D imaging Lidar system.