针对高能X射线成像模糊、噪声强等问题，研究基于MCMC-Bayesian的X射线图像重建和数据分析的高效方法。首先，分别对理想状态下的平行束和实际应用中的锥形束投影下的X射线成像进行建模，结合低秩先验和多尺度迭代实现X射线图像的盲去卷积。然后分别研究两种光束投影下的X射线图像重建的贝叶斯建模方法。对于平行束投影下的重建模型，提出结合先验约束和边界增强的Gibbs抽样方法，以获得重建图像的最佳估计；对于锥形束投影下的重建模型，提出基于Gibbs范围内的M-H抽样策略的PMCMC算法，对高维的参数空间非线性模型进行求解。最后，提出基于超分辨率重建的NSCT域模极大值算法实现客体界面的亚像素级精确检测，并估计客体的密度分布参量。本项目的研究在求解非线性高维反演问题上是一种新颖的有益尝试，将进一步提高核武器初级诊断客体界面和密度测量的精度，对我国国防建设和科研水平的提升都具有重要的研究意义。

Aiming at the problems of high-energy X-ray such as imaging blur and serious noise, the efficient methods of X-ray image reconstruction and data analysis based on the MCMC-Bayesian method are studied. Firstly, the X-ray imaging of the parallel beam projection under the ideal condition and the cone beam projection for the practical application are modeled respectively, and the low-rank prior and multiscale iteration are combined for blind deconvolution of X-ray image. Then the Bayesian modeling methods for X-ray image reconstruction under two beam projections are studied respectively. For the reconstruction model with parallel beam projection, a Gibbs sampling method combining priori constraints and boundary enhancement is proposed to obtain the optimal estimation of the reconstructed image; for the reconstruction model with cone beam projection, a PMCMC algorithm based on Gibbs-based M-H sampling strategy is proposed to solve the high-dimensional nonlinear parametric space model. Finally, the NSCT domain modulus maximum algorithm based on super-resolution reconstruction is proposed to realize the object boundary of sub-pixel precise detection, and estimate the density distribution of the object. Our project is a novel and useful attempt to solve the nonlinear high-dimensional inversion problem. It will further improve the accuracy of the primary diagnostic object interface and density measurement of nuclear weapons, and plays an important role in promoting and developing the national defense and scientific research of our country.