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14 August 2019 A depth image reconstruction algorithm based on background noise censoring
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Proceedings Volume 11179, Eleventh International Conference on Digital Image Processing (ICDIP 2019); 111790Q (2019) https://doi.org/10.1117/12.2539662
Event: Eleventh International Conference on Digital Image Processing (ICDIP 2019), 2019, Guangzhou, China
Abstract
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.
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Pengwei Huang, Weiji He, Guohua Gu, and Qian Chen "A depth image reconstruction algorithm based on background noise censoring", Proc. SPIE 11179, Eleventh International Conference on Digital Image Processing (ICDIP 2019), 111790Q (14 August 2019); https://doi.org/10.1117/12.2539662
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