Double-regularization finite angle CT image reconstruction method based on tight wavelet frame

Abstract

The invention relates to a double-regularization finite angle CT image reconstruction method based on a tight wavelet frame, and belongs to the field of image processing. The method comprises the following steps: S1, collecting projection data; S2, establishing a double-regularization finite angle CT reconstruction model; S3, performing finite angle CT iterative reconstruction; S4, outputting a reconstructed image; and when the iterative reconstruction algorithm in the step S3 converges, outputting a reconstructed image. The double-regularization finite-angle CT image reconstruction method based on the tight wavelet frame comprises the steps that hard threshold processing is conducted on a high-frequency part, image TV minimization smoothing processing is conducted on a low-frequency part,and after the double-regularization mechanism processing is conducted, finite-angle artifacts and noise of a CT image can be effectively restrained; and the boundary can be effectively protected, sothat the quality of the CT reconstructed image is improved.

Description

technical field [0001] The invention belongs to the field of image processing, and relates to a double-regularized limited-angle CT image reconstruction method based on a compact wavelet frame. Background technique [0002] Restricted by factors such as the scanning environment and the structure of the scanned object, sometimes the object to be inspected can only be scanned within a certain angle range, and the projection data collected at this time is incomplete. For example: in-service pipeline imaging, large-scale object detection, C-arm CT (Computed Tomography), etc. This scanning situation is called a limited-angle CT scan. For the reconstruction problem of limited-angle CT scan, the traditional filtered back-projection reconstruction algorithm will cause many artifacts in the reconstructed image, resulting in the loss or concealment of some important structural information, which seriously affects the accuracy of non-destructive testing or the doctor's diagnosis of di...

AI Technical Summary

Problems solved by technology

However, the following defects still exist: the method described in the above patent application only considers the x and y axes of the image to perform gradient L0 boundary-preserving diffusion correction, but does not take into account the multi-directional characteristics of the artifacts of limited-angle CT images

However, the following defects still exist: (1) the method described in the above-mentioned patent application only considers the gradient transformation of the image, and only the high-frequency information of the high-frequency part of the image; (2) the method described in the above-mentioned patent application considers curvature constraints, and only considers high-frequency frequency profile change

However, the following defects still exist: the method described in the above-mentioned patent application only considers artifacts in the x and y-axis directions, but does not take into account that the artifacts of limited-angle CT images have multi-directional characteristics

The gradient transformation of the image only has the high-frequency information of the high-frequency part of the image, and the image with limited-angle artifacts not only has artifacts in the high-frequency part, but also has distortion in the low-frequency part. The method described in the above patent application does not take into account the multi-scale and multi-resolution images rate characteristics

[0007] Most existing finite-angle CT optimal reconstruction methods either only consider constraining the high-frequency part of the image, or do not consider the artifacts of the finite-angle CT reconstruction image. Under multi-scale and multi-resolution decomposition, not only the high-frequency part, There is also a low-frequency part, or the artifacts in the low-frequency part are not considered to be effectively processed

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