A sharp edge-preserving electrical resistance tomography image reconstruction method

Abstract

The invention discloses a sharp edge preserving resistance tomography image reconstruction method. The method comprises the steps of obtaining a relative boundary measurement value vector and a sensitivity matrix required by reconstruction according to a measured field domain; Setting initialization parameters; Determining an optimal regularization parameter by using an improved GCV method; Calculating a gradient and a Hessian matrix of the objective function; Updating the value of the solution by using a Gauss-Newton iteration method; Judging whether iteration is finished or not; And performing imaging according to the finally solved imaging gray value. According to the method, the defects that the edge of a reconstructed image is fuzzy and the image resolution is low in a traditional Tikhonov regularization algorithm and a total variation regularization algorithm are overcome, and the method has a good effect on improving the quality of an electrical tomography reconstructed image and keeping the keeping capability of a sharp edge.

Description

technical field [0001] The invention belongs to the technical field of electrical tomography, and in particular relates to a sharp edge-preserving electric resistance tomography image reconstruction method. Background technique [0002] Electrical Tomography (ET) appeared in the late 1980s. It is a process tomography technology based on the sensitive mechanism of electrical characteristics. The mathematical model corresponding to the image reconstruction problem belongs to the field of inverse problem solving. The image reconstruction process It is the solution process of the inverse problem. In view of the serious ill-conditionedness in the solution of the inverse problem, it is necessary to constrain the solution by selecting an appropriate regularization method. The idea of ​​the regularization method is to find a stable solution set constrained by prior information to approximate the real solution. Different selection of prior information and different forms of regular...

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Problems solved by technology

[0003]Due to the existence of the ladder effect, the sharp edge effect of the full variation regularization algorithm and the quality of the reconstructed image are reduced. In order to improve the quality of the reconstructed image and the ability to maintain sharp edges , the present invention proposes a sharp edge-preserving electrical resistance tomography image reconstruction method to solve the inverse problem of electrical tomography

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