Robust Bayesian Target Detection Algorithm for Depth Imaging From Sparse Single-Photon Data
Robust Bayesian Target Detection Algorithm for Depth Imaging From Sparse Single-Photon Data
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DOI：
10.1109/tci.2016.2618323
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发表时间：
2016-01
影响因子：
5.4
通讯作者：
Y. Altmann;Ximing Ren;A. Mccarthy;G. Buller;S. Mclaughlin
中科院分区：
文献类型：
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作者：
Y. Altmann;Ximing Ren;A. Mccarthy;G. Buller;S. Mclaughlin
本文提出了一种新的贝叶斯模型和相关算法，利用时间相关的单光子计数测量的全波形，在非常低的光子计数极限(即通常每像素少于20个光子)下，进行深度和强度分布。该模型将每个激光雷达波形表示为一个未知的恒定背景电平，在目标存在的情况下，该背景电平被组合成一个已知的脉冲响应，该脉冲响应由目标强度加权，最后被泊松噪声污染。将联合目标检测和深度成像问题表示为一个像素化的模型选择和估计问题，并利用贝叶斯推理进行求解。关于该问题的先验知识被嵌入到一个分层模型中，该模型描述了模型参数之间的依赖结构，同时考虑了它们的约束。特别是，马尔可夫随机场(MRF)被用来对背景水平和目标存在标签的联合分布进行建模，这两者都被期望表现出显著的空间相关性。然后提出了一种包括可逆跳跃更新的自适应马尔可夫链蒙特卡罗算法来计算感兴趣的贝叶斯估计。该算法具有随机优化自适应机制，通过最大边际似然估计自动调整MRF的参数。最后，利用真实数据进行了一系列实验，验证了该方法的有效性。
This paper presents a new Bayesian model and associated algorithm for depth and intensity profiling using full waveforms from time-correlated single-photon counting measurements in the limit of very low photon counts (i.e., typically less than 20 photons per pixel). The model represents each Lidar waveform as an unknown constant background level, which is combined in the presence of a target, to a known impulse response weighted by the target intensity and finally corrupted by Poisson noise. The joint target detection and depth imaging problem is expressed as a pixelwise model selection and estimation problem, which is solved using Bayesian inference. Prior knowledge about the problem is embedded in a hierarchical model that describes the dependence structure between the model parameters while accounting for their constraints. In particular, Markov random fields (MRFs) are used to model the joint distribution of the background levels and of the target presence labels, which are both expected to exhibit significant spatial correlations. An adaptive Markov chain Monte Carlo algorithm including reversible-jump updates is then proposed to compute the Bayesian estimates of interest. This algorithm is equipped with a stochastic optimization adaptation mechanism that automatically adjusts the parameters of the MRFs by maximum marginal likelihood estimation. Finally, the benefits of the proposed methodology are demonstrated through a series of experiments using real data.