Proc. SPIE. 10806, Tenth International Conference on Digital Image Processing (ICDIP 2018)
KEYWORDS: 3D image processing, 3D acquisition, Photon counting, Target detection, Detection and tracking algorithms, Signal detection, Denoising, Time correlated photon counting, Sensors, Imaging systems
The active 3D lidar imaging system usually spends a long time sampling many points for each spatial pixel in the target scene by raster scanning and generating a statistic histogram of photon counting. By relying on a variety of effective imaging algorithms, it extracts the depth, reflectivity and other information of target to reconstruct the 3D scene image. Since signal photons will be clustered together near the truth depth, so we set a window to gather reflected signal photons. We propose a new denoising algorithm based on photon-counting without generating photon counting statistic histogram in order to get 3D image of targets quickly. To validate the new theory in this paper, we designed a contrast test. Experimental results demonstrate that this imaging method can suppress the noise while acquiring the scene depth and reduce the sampling time at low light level. The imaging accuracy of our method is increased by over 6-fold more than the maximum likelihood estimation and improving imaging performance significantly.