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A 3D denoising algorithm based on photon-counting imaging at low light level
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Paper Abstract

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.

Paper Details

Date Published: 9 August 2018
PDF: 10 pages
Proc. SPIE 10806, Tenth International Conference on Digital Image Processing (ICDIP 2018), 108063N (9 August 2018); doi: 10.1117/12.2503117
Show Author Affiliations
Changqiang Wu, Nanjing Univ. of Science and Technology (China)
Weiji He, Nanjing Univ. of Science and Technology (China)
Guohua Gu, Nanjing Univ. of Science and Technology (China)
Qian Chen, Nanjing Univ. of Science and Technology (China)


Published in SPIE Proceedings Vol. 10806:
Tenth International Conference on Digital Image Processing (ICDIP 2018)
Xudong Jiang; Jenq-Neng Hwang, Editor(s)

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