基于贝叶斯自适应估计的光子计数集成成像

针对光子数极少环境下三维目标的重构问题,基于光子计数集成成像系统提出了一种贝叶斯自适应估计方法,来提高三维目标深度切片的重构质量。首先,通过光子计数集成成像系统获得一系列光子计数元素图像。接着,从光子计数过程的泊松分布出发,利用集成成像系统中对于同一个目标像素的多次采样特性,引入了局部自适应均值因子,从而建立起元素图像像素光子数估计的单参数后验概率模型。最后,通过后验概率模型的均值计算获得更新后的光子计数元素图像,并基于光束可逆原理重构出深度切片图像。实验结果表明:采用该方法在场景的两个深度处重构的切片图像相比传统贝叶斯重构图像的峰值信噪比提高了7.4dB和8.5dB,极大地提升了微弱光三维目标的重构质量。

Photon counting integral imaging based on adaptive Bayesian estimation

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.