28 May 2013 Point Spread Function (PSF) noise filter strategy for geiger mode LiDAR
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LiDAR is an efficient optical remote sensing technology that has application in geography, forestry, and defense. The effectiveness is often limited by signal-to-noise ratio (SNR). Geiger mode avalanche photodiode (APD) detectors are able to operate above critical voltage, and a single photoelectron can initiate the current surge, making the device very sensitive. These advantages come at the expense of requiring computationally intensive noise filtering techniques. Noise is a problem which affects the imaging system and reduces the capability. Common noise-reduction algorithms have drawbacks such as over aggressive filtering, or decimating in order to improve quality and performance. In recent years, there has been growing interest on GPUs (Graphics Processing Units) for their ability to perform powerful massive parallel processing. In this paper, we leverage this capability to reduce the processing latency. The Point Spread Function (PSF) filter algorithm is a local spatial measure that has been GPGPU accelerated. The idea is to use a kernel density estimation technique for point clustering. We associate a local likelihood measure with every point of the input data capturing the probability that a 3D point is true target-return photons or noise (background photons, dark-current). This process suppresses noise and allows for detection of outliers. We apply this approach to the LiDAR noise filtering problem for which we have recognized a speed-up factor of 30-50 times compared to traditional sequential CPU implementation.
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O'Neil Smith, O'Neil Smith, Robert Stark, Robert Stark, Philip Smith, Philip Smith, Randall St. Romain, Randall St. Romain, Steven Blask, Steven Blask, "Point Spread Function (PSF) noise filter strategy for geiger mode LiDAR", Proc. SPIE 8731, Laser Radar Technology and Applications XVIII, 87310A (28 May 2013); doi: 10.1117/12.2016116; https://doi.org/10.1117/12.2016116

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