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

Changqiang Wu; Weiji He; Guohua Gu; Qian Chen

doi:10.1117/12.2503117

9 August 2018 A 3D denoising algorithm based on photon-counting imaging at low light level

Changqiang Wu, Weiji He, Guohua Gu, Qian Chen

Proceedings Volume 10806, Tenth International Conference on Digital Image Processing (ICDIP 2018); 108063N (2018) https://doi.org/10.1117/12.2503117
Event: Tenth International Conference on Digital Image Processing (ICDIP 2018), 2018, Shanghai, China

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.

Citation Download Citation

Changqiang Wu, Weiji He, Guohua Gu, and Qian Chen "A 3D denoising algorithm based on photon-counting imaging at low light level", Proc. SPIE 10806, Tenth International Conference on Digital Image Processing (ICDIP 2018), 108063N (9 August 2018); https://doi.org/10.1117/12.2503117

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KEYWORDS

3D acquisition

3D image processing

Photon counting

Target detection

Detection and tracking algorithms

Signal detection

Denoising

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