X-ray image linear reconstruction method

Abstract

The invention discloses an X-ray image linear reconstruction method, which comprises the steps: obtaining an X-ray projection image, introducing a regular term based on total variation prior, and constructing a reconstruction target function; introducing super prior parameters, and constructing a layered Bayesian model; introducing a split variable by using a variable splitting method, and separating a data fidelity term and a TV regular term to obtain joint probability density distribution in a split form; defining a super-prior variable based on Jefferys prior to obtain condition distribution of each variable; iteratively updating the super-prior parameters, and solving conditional distribution of split variables containing TV regular terms; approximating the full-condition probability density distribution of the to-be-solved parameter by utilizing the low-rank property of the forward matrix, and calculating the target distribution of low-rank approximation to obtain a closed solution about the to-be-solved parameter; and calculating the mean value of the sampling samples, and estimating the to-be-solved parameters. According to the method, the problems of high calculation overhead and the like in solving the large-scale linear inverse problem can be effectively solved.

Description

technical field [0001] The invention belongs to the technical field of image processing and relates to an X-ray image reconstruction method. Background technique [0002] X-ray imaging technology is an important means to study the internal structure of nuclear weapons, and is the main tool to obtain the physical and geometric properties of the core in the late stage of the non-nuclear implosion evolution process. In the diagnostic research of high-density materials by X-ray imaging technology, there are two main goals: one is to accurately measure the internal space density and distribution of the object, and the other is to quantitatively determine the geometric interface inside the object. The density and interface measurement of the object are typical high-dimensional inversion problems, and there are problems such as high data dimensionality, which is easily affected by system ambiguity such as noise, scattering, light source and detector ambiguity. Image reconstruction...

AI Technical Summary

Problems solved by technology

[0004] The technical problem to be solved by the present invention is: the calculation cost of X-ray image reconstruction is easily affected by factors such as noise, and a method for linear reconstruction of X-ray images is provided, which can accelerate the X-ray image reconstruction process and improve the density reconstruction accuracy of X-ray images

Images