光学精密工程2018，Vol.26Issue(3)：565-571,7.DOI:10.3788/OPE.20182603.0565
基于贝叶斯自适应估计的光子计数集成成像
Photon counting integral imaging based on adaptive Bayesian estimation
摘要
Abstract
A novel method of Bayesian adaptive estimation was proposed to improve reconstructed slice images based on a photon-counting integral imaging system for three-dimensional (3D) targets in a photon-starved environment.First,a series of photon-counted elemental images were obtained by a photon-counting integral imaging system.Subsequently,based on the Poisson distribution of the photon-counting process,the posterior probability model for photon estimation of the elemental images was established with one local adaptive mean value introduced.The model benefits from the feature of multiple sampling for the same reconstructed voxel by the integral imaging system.Finally,the photon-counted elemental images were updated by calculating the expected value of the posterior probability model and the depth slice images were reconstructed by back-propagating the captured light rays.Experimental results show that the peak signal-to-noise ratio of the depth slice images reconstructed by the proposed method can be 7.4 dB and 8.5 dB higher than that of conventional Bayesian estimation at two scene depths,which greatly improves the quality of 3D target reconstruction.关键词
光子计数/深度切片/贝叶斯估计/自适应均值/集成成像Key words
photon counting/depth slice/Bayesian estimation/adaptive mean/integral imaging分类
数理科学引用本文复制引用
戚佳佳,顾国华,陈远金,何伟基,陈钱..基于贝叶斯自适应估计的光子计数集成成像[J].光学精密工程,2018,26(3):565-571,7.基金项目
国家自然科学基金资助项目(No.61271332) （No.61271332）
中央高校基本科研业务费专项资金资助项目(No.30920140112012) （No.30920140112012）
