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| 基于先验边缘结构引导的动态原位力学CT稀疏角度重建方法 |
| Sparse Angle CT Reconstruction Method Based on Prior Edge Structure Guidance in Dynamic in-situ Mechanical |
| 投稿时间：2024-03-24 修订日期：2024-05-23 |
| DOI： |
| 中文关键词: 动态原位实验 CT 稀疏重建 实验力学 结构演化 |
| 英文关键词:dynamic in-situ experiment CT sparse angle reconstruction experimental mechanics structure evolution |
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| 中文摘要: |
| 动态原位力学CT实验是一种利用先进光源无损非接触式测量手段，实时捕捉材料在外场作用下内部结构演化三维图像的方法，其结合了原位力学实验和计算机断层扫描技术（Computed Tomography，CT），对于研究材料内部演化力学机制具有重要应用。然而原位实验中材料内部结构演化速率与CT扫描时间存在矛盾，导致往往仅能进行稀疏角度采样；同时，稀疏角度采样导致的重建噪声，会影响力学指标的判断和提取。针对上述问题，本文提出一种基于先验边缘结构引导的动态原位力学CT稀疏角度重建方法（Directed Total Variation，DTV），该方法引入卷积神经网络噪声学习方法获得先验边缘结构信息，从而引导全变分去噪方向，提高动态原位力学CT表征的重建质量。 |
| 英文摘要: |
| Dynamic in-situ mechanical CT experiment is a method that uses advanced light sources to capture real-time 3D images of the internal structure evolution of materials under external fields.It is an non-destructive and non-contact measurement methods. It combines in-situ mechanical experiments and computed tomography (CT) technology, and has important application in the research of internal evolution mechanical mechanisms. However, during the in-situ experiment, there is a contradiction between the internal structure evolution rate and the time of CT scan, hence only sparse projections can be collected. In addition, the reconstruction noise caused by sparse angle sampling will affect the judgment and extraction of mechanical indicators. To address these issues, this paper proposes a dynamic in-situ mechanical CT sparse angle reconstruction method (Directed Total Variation, DTV) based on prior edge structure guidance. This method achieves high-quality dynamic in-situ mechanical CT characterization by introducing noise learning method in convolutional neural net to obtain prior edge structure information, which guides the direction of total variation denoising. |
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