We introduce a new approach to depth and reflectivity estimation that emphasizes the unmixing of contributions from signal and noise sources. At each pixel in an image, short-duration range gates are adaptively determined and applied to remove detections likely to be due to noise. For pixels with too few detections to perform this censoring accurately, data are combined from neighboring pixels to improve depth estimates, where the neighborhood formation is also adaptive to scene content.
Using detector arrays can speed up lidar systems by parallelizing acquisition. However, current SPAD arrays have time bins longer than typical laser pulse durations, resulting in measurement errors dominated by quantization. We propose an optical time-of-flight system that uses subtractive dither to improve image depth resolution. Additional modeling of the measurement noise with a generalized Gaussian distribution leads to more efficient order statistics-based estimators and rules of thumb for when subtractive dither is useful and which estimator to apply.
Dead time effects have been considered a major limitation for fast data acquisition in various time-correlated single photon counting applications, since a commonly adopted approach for dead time mitigation is to operate in the low-flux regime where dead time effects can be ignored. Through the application of lidar ranging, this work explores the empirical distribution of detection times in the presence of dead time and demonstrates that an accurate statistical model can result in reduced ranging error with faster data acquisition time when operating in the high-flux regime. Specifically, we show that the empirical distribution of detection times converges to the stationary distribution of a Markov chain. Depth estimation can then be performed by passing the empirical distribution through a filter matched to the stationary distribution. Moreover, based on the Markov chain model, we formulate the recovery of arrival distribution from detection distribution as a nonlinear inverse problem and solve it via provably convergent mathematical optimization.