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In conventional 3D imaging, a large number of detected photons is required at each pixel to mitigate the effect of signal-dependent Poisson or shot noise. Parametric Poisson process imaging (PPPI) is a new framework that enables scene depth acquisition with very few detected photons despite significant contribution from background light. Our proposed computational imager is based on accurate physical...
We present a new method for simultaneously denoising and unwrapping phase in multi-frequency homodyne time-of-flight ranging for the formation of accurate depth maps despite low SNR of raw measurements. This is achieved with a new generalized approximate message passing (GAMP) algorithm for minimum mean-squared error estimation of the phase. A detailed, physically-accurate acquisition model is central...
Light detection and ranging (LIDAR) systems use time of flight (TOF) in combination with raster scanning of the scene to form depth maps, and TOF cameras instead make TOF measurements in parallel by using an array of sensors. Here we present a framework for depth map acquisition using neither raster scanning by the illumination source nor an array of sensors. Our architecture uses a spatial light...
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