The photon counting lidar system with a single photon detector as the core device can reconstruct the target image under low light or high background noise environment. The traditional imaging method requires a long integration time to acquire the target point cloud data and the target depth is extracted by the photon counting distribution histogram. We propose a reconstruction algorithm for photon-counting depth images based on background noise censoring to overcome the shortcomings of traditional methods. According to the different distribution characteristics of background photons and signal photons in time domain, we set a window to examine the photon flight data in this window and choose appropriate thresholds to find signal photon units, then we use computational imaging method to further smooth the reconstructed image. By calculating the root mean square error (RMSE) of reconstructing images using different algorithms, it is known that the results of reconstructing images using proposed algorithm are better than those using traditional maximum likelihood estimation (MLE) algorithm, the imaging accuracy of our method is increased by over 1.4-fold more than the maximum likelihood estimation and improving imaging performance significantly. The experimental results show that the proposed algorithm can effectively improve the reconstructed image of photon counting lidar, and it has positive significance for expanding the application range of photon counting lidar.
KEYWORDS: Reconstruction algorithms, Integral imaging, Detection and tracking algorithms, Electron multiplying charge coupled devices, Charge-coupled devices, Image quality, Image processing, 3D acquisition, 3D image processing, 3D displays
Electron-multiplying charge-coupled device (EMCCD) has the characteristic of single photon response under a low-light environment. It is proposed that the reconstruction algorithm of low-light integral imaging by EMCCD reconstruct the details of the target under a low-light environment. First, the algorithm acquires a series of element images by EMCCD integral imaging system. Second, as grayscale values of different element images of the same target meet Poisson distribution, the algorithm introduces a local self-adaptive factor and derives the posterior probability distribution of grayscale value of the target. Finally, it calculates the new element images by posterior probability distribution and reconstructs the target image by updated element images. Experimental results show that the peak signal-to-noise ratio of the reconstructed image by the proposed method is 4.3 dB higher than that of conventional Bayesian estimation. Considering the reconstructed image quality and computational complexity, the overall quality of the reconstructed image is the best when using the 7 × 7 neighborhood range to calculate the local self-adaptive factor in the algorithm. Experimental results show that the proposed algorithm greatly improves the quality of the reconstructed image of the target under low-light environment.