摘要
针对高能闪光X射线图像线性重建结果受系统模糊影响的问题,提出一种随机扰动优化和多模型融合的非线性重建算法。构建非线性正向模型并推导相应的雅可比矩阵形式,结合贝叶斯理论考虑该反演问题的求解及不确定量化,引入基于弱信息先验的超参数构建非线性分层贝叶斯模型。通过加速求解随机扰动的优化问题对条件分布进行采样,结合雅可比矩阵投影约束该优化问题的求解,并设计目标参数的提议分布以减小样本统计偏差。此外,提出一种多模型融合策略,在最小方差准则下融合线性与非线性贝叶斯模型的样本值,提高样本估计效率的同时确保重建结果呈现清晰的边缘和较高的精度。实验结果表明,该算法可以有效抑制系统模糊及噪声的影响,相比于线性重建算法可以得到更加准确的重建结果。
Aiming at the problem that the linear reconstruction results of high energy flash X-ray images are affected by system blur,a nonlinear reconstruction algorithm with randomly perturbed optimization and multi-models fusion is proposed.The nonlinear forward model of high energy flash radiography is constructed and the Jacobian matrix of residual vector of the objective function is derived.The solution and uncertain quantification of the inverse problem are considered from the perspective of Bayesian theory,and the nonlinear hierarchical Bayesian model is constructed by introducing weak information prior-based hyper-parameters.The hyper-parameters can avoid manual adjustment of parameters and are not affected by changes in parameter form,and can obtain more accurate parameter estimation results.By accelerating the solution of the randomly perturbed optimization problem the conditional distribution is sampled,and the Jacobian matrix projection-based constraint is used to solve the optimization problem.The proposal distribution of the object parameter is designed to reduce the statistical deviation of samples.In addition,a multi-model fusion strategy is proposed to fuse the sample values from linear and nonlinear Bayesian models under the minimum variance criterion.Surrogate model with strong correlation and physical properties is selected and directly carried out on the expectation estimation.The proposed algorithm improves the efficiency of sample estimation while ensuring that the reconstructed results show clear edges and high accuracy.Nonlinear reconstruction experiment is carried out on high-energy flash X-ray static images under 4 MeV energy level,and compared with the existing reconstruction algorithms based on uncertainty analysis to verify the effectiveness of the proposed algorithm.The irradiated target is an inverted cone,which is made of tin and placed on the center of device.Compared with the linear reconstruction results,the proposed algorithm can effectively suppress the background noise of image and obtain better visual effects in the isosceles region of the cone.Experimental results show that the algorithm can effectively suppress the influence of system ambiguity and noise,and can obtain more accurate reconstruction results than linear reconstruction algorithms.
作者
许金鑫
李庆武
管志强
王肖霖
XU Jinxin;LI Qingwu;GUAN Zhiqiang;WANG Xiaolin(Nanjing Marine Radar Institute,Nanjing 211106,China;College of Internet of Things Engineering,Hohai University,Changzhou,Jiangsu 213002,China)
出处
《光子学报》
EI
CAS
CSCD
北大核心
2022年第3期62-75,共14页
Acta Photonica Sinica
基金
国家自然科学基金(No.U1830105)。
关键词
高能闪光X射线照相
非线性重建
随机扰动优化
多模型融合
不确定度量化
High energy flash X-radiography
Nonlinear reconstruction
Randomly perturbed optimization
Multi-models fusion
Uncertainty quantification
作者简介
第一作者:许金鑫(1993-),男,工程师,博士,主要研究方向为高能闪光X射线图像反问题。Email:2019377631@qq.com;通讯作者:李庆武(1964-),男,教授,博士,主要研究方向为视觉感知与人工智能、水下环境成像探测、输配电智能感知。Email:li_qingwu@163.com。
