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首页> 外文期刊>IEEE sensors journal >A Deep Learning Compensated Back Projection for Image Reconstruction of Electrical Capacitance Tomography
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A Deep Learning Compensated Back Projection for Image Reconstruction of Electrical Capacitance Tomography

机译：电容断层扫描图像重建的深度学习补偿后投影

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摘要

The linear back projection (LBP) algorithm is often used for real-time online image reconstruction of electrical capacitance tomography (ECT) due to its high speed. However, due to the fact that the image reconstruction of ECT is a nonlinear ill-posed inverse problem, reconstructed images obtained by the LBP algorithm that simplifies ECT image reconstruction as a linear problem, tend to have distortion and can only be used for qualitative observation. In this paper, a deep fully-connected neural network, which improves the imaging quality of the LBP algorithm by compensating its imaging results is proposed. Instead of simplifying the ECT image reconstruction as a linear problem, our proposed compensated LBP algorithm uses a deep neural network to map the nonlinear relationship from capacitance to permittivity distribution. Furthermore, the difference between the capacitance regarding the permittivity distribution reconstructed by the LBP algorithm and the actual capacitance is used as the input of the network while the difference between the reconstructed permittivity distribution and the actual permittivity distribution is used as the output of the network. The results of the network can be used to compensate the image reconstruction results of the LBP. This strategy makes the ECT image reconstruction need only to deal with a support interval significantly smaller than that of the original ECT image reconstruction problem and is helpful to suppress the nonlinearity to be trained. Both the training and testing results based on simulation data instances and experimental data show that the proposed compensation network has a great improvement on image reconstruction results of the LBP algorithm. In addition, the computation load is comparable to the original LBP algorithm.
机译：线性背部投影（LBP）算法通常用于电容断层扫描（ECT）的实时在线图像重建由于其高速。但是，由于ECT的图像重建是非线性不良逆问题的事实，由LBP算法获得的重建图像简化了ECT图像重建作为线性问题，倾向于具有失真，并且只能用于定性观察。在本文中，提出了一种深度全连接的神经网络，其通过补偿其成像结果来提高LBP算法的成像质量。而不是将ECT图像重建简化为线性问题，而是我们提出的补偿LBP算法使用深神经网络来将非线性关系从电容映射到允许分布。此外，关于由LBP算法重建的介电常数分布的电容与实际电容之间的差异用作网络的输入，而重构介电常数分布和实际介电常数分布之间的差异用作网络的输出。网络的结果可用于补偿LBP的图像重建结果。该策略使得ECT图像重建仅需要处理显着小于原始ECT图像重建问题的支持间隔，并且有助于抑制要训练的非线性。基于仿真数据实例和实验数据的培训和测试结果都表明，所提出的补偿网络对LBP算法的图像重建结果具有很大改进。另外，计算负载与原始LBP算法相当。

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