scholarly journals Application of a Generative Adversarial Network in Image Reconstruction of Magnetic Induction Tomography

Sensors ◽  
2021 ◽  
Vol 21 (11) ◽  
pp. 3869
Author(s):  
Dan Yang ◽  
Jiahua Liu ◽  
Yuchen Wang ◽  
Bin Xu ◽  
Xu Wang
Keyword(s):  
Image Reconstruction ◽  
Magnetic Induction ◽  
Mean Squared Error ◽  
Voltage Measurement ◽  
Generative Adversarial Network ◽  
Conductivity Distribution ◽  
Adversarial Network ◽  
Non Linear ◽  
Ill Posed ◽  
Magnetic Induction Tomography

Image reconstruction of Magnetic induction tomography (MIT) is an ill-posed problem. The non-linear characteristics lead many difficulties to its solution. In this paper, a method based on a Generative Adversarial Network (GAN) is presented to tackle these barriers. Firstly, the principle of MIT is analyzed. Then the process for finding the global optimum of conductivity distribution is described as a training process, and the GAN model is proposed. Finally, the image was reconstructed by a part of the model (the generator). All datasets are obtained from an eight-channel MIT model by COMSOL Multiphysics software. The voltage measurement samples are used as input to the trained network, and its output is an estimate for image reconstruction of the internal conductivity distribution. The results based on the proposed model and the traditional algorithms were compared, which have shown that average root mean squared error of reconstruction results obtained by the proposed method is 0.090, and the average correlation coefficient with original images is 0.940, better than corresponding indicators of BPNN and Tikhonov regularization algorithms. Accordingly, the GAN algorithm was able to fit the non-linear relationship between input and output, and visual images also show that it solved the usual problems of artifact in traditional algorithm and hot pixels in L2 regularization, which is of great significance for other ill-posed or non-linear problems.

scholarly journals Comparison of Algebraic Reconstruction Technique Methods and Generative Adversarial Network in Image Reconstruction of Magnetic Induction Tomography (MIT)

2021 ◽  
Vol 2071 (1) ◽  
pp. 012044
Author(s):  
A J Lubis ◽  
N F Mohd Nasir ◽  
Z Zakaria ◽  
M Jusoh ◽  
M M Azizan ◽  
...  
Keyword(s):  
Neural Network ◽  
Regularization Method ◽  
Tikhonov Regularization Method ◽  
Reconstruction Technique ◽  
Algebraic Reconstruction Technique ◽  
Training Process ◽  
Generative Adversarial Network ◽  
Adversarial Network ◽  
Non Linear ◽  
Magnetic Induction Tomography

Abstract Magnetic induction tomography (MIT) is a technique used for imaging electromagnetic properties of objects using eddy current effects. The non-linear characteristics had led to more difficulties with its solution especially in dealing with low conductivity imaging materials such as biological tissues. Two methods that could be applied for MIT image processing which is the Generative Adversarial Network (GAN) and the Algebraic Reconstruction Technique (ART). ART is widely used in the industry due to its ability to improve the quality of the reconstructed image at a high scanning speed. GAN is an intelligent method which would be able to carry out the training process. In the GAN method, the MIT principle is used to find the optimum global conductivity distribution and it is described as a training process and later, reconstructed by a generator. The output is an approximate reconstruction of the distribution’s internal conductivity image. Then, the results were compared with the previous traditional algorithm, namely the regularization algorithm of BPNN and Tikhonov Regularization method. It turned out that GAN had able to adjust the non-linear relationship between input and output. GAN was also able to solve non-linear problems that cannot be solved in the previous traditional algorithms, namely Back Propagation Neural Network (BPNN) and Tikhonov Regularization method. There are several other intelligent algorithms such as CNN (Convolution Neural Network) and K-NN (K-Nearest Neighbor), but such algorithms have not been able to produce the expected image quality. Thus, further study is still needed for the improvement of the image quality. The expected result in this study is the comparison of these two techniques, namely ART and GAN to get the best results on the image reconstruction using MIT. Thus, it is shown that GAN is a better candidate for this purpose.

Image Reconstruction with the Fourier Coefficients for Magnetic Induction Tomography

2020 ◽  
Vol 16 (2) ◽  
pp. 156-163
Author(s):  
Jingwen Wang ◽  
Xu Wang ◽  
Dan Yang ◽  
Kaiyang Wang
Keyword(s):  
Inverse Problem ◽  
Image Reconstruction ◽  
Magnetic Induction ◽  
Fourier Coefficients ◽  
Signal Reconstruction ◽  
Reconstruction Algorithm ◽  
Reconstruction Method ◽  
Measurement Equation ◽  
Image Reconstruction Method ◽  
Magnetic Induction Tomography

Background: Image reconstruction of magnetic induction tomography (MIT) is a typical ill-posed inverse problem, which means that the measurements are always far from enough. Thus, MIT image reconstruction results using conventional algorithms such as linear back projection and Landweber often suffer from limitations such as low resolution and blurred edges. Methods: In this paper, based on the recent finite rate of innovation (FRI) framework, a novel image reconstruction method with MIT system is presented. Results: This is achieved through modeling and sampling the MIT signals in FRI framework, resulting in a few new measurements, namely, fourier coefficients. Because each new measurement contains all the pixel position and conductivity information of the dense phase medium, the illposed inverse problem can be improved, by rebuilding the MIT measurement equation with the measurement voltage and the new measurements. Finally, a sparsity-based signal reconstruction algorithm is presented to reconstruct the original MIT image signal, by solving this new measurement equation. Conclusion: Experiments show that the proposed method has better indicators such as image error and correlation coefficient. Therefore, it is a kind of MIT image reconstruction method with high accuracy.

Image Reconstruction Algorithm Based on Improved Super-Resolution Generative Adversarial Network

2021 ◽  
Vol 58 (8) ◽  
pp. 0810005
Author(s):  
查体博 Zha Tibo ◽  
罗林 Luo Lin ◽  
杨凯 Yang Kai ◽  
张渝 Zhang Yu ◽  
李金龙 Li Jinlong
Keyword(s):  
Image Reconstruction ◽  
Super Resolution ◽  
Reconstruction Algorithm ◽  
Generative Adversarial Network ◽  
Image Reconstruction Algorithm ◽  
Adversarial Network

scholarly journals DRGAN: a deep residual generative adversarial network for PET image reconstruction

2020 ◽  
Vol 14 (9) ◽  
pp. 1690-1700
Author(s):  
Qianqian Du ◽  
Yan Qiang ◽  
Wenkai Yang ◽  
Yanfei Wang ◽  
Yong Ma ◽  
...  
Keyword(s):  
Image Reconstruction ◽  
Generative Adversarial Network ◽  
Adversarial Network ◽  
Pet Image Reconstruction

Development and investigation of a deep learning based method for TEC map completion

2020 ◽  
Author(s):  
Mingwu Jin ◽  
Yang Pan ◽  
Shunrong Zhang ◽  
Yue Deng
Keyword(s):  
Solar Activity ◽  
Mean Squared Error ◽  
Image Inpainting ◽  
Science Research ◽  
High Solar Activity ◽  
Electron Content ◽  
International Gnss Service ◽  
Generative Adversarial Network ◽  
Total Electron ◽  
Adversarial Network

<p>Because of the limited coverage of receiver stations, current measurements of Total Electron Content (TEC) by ground-based GNSS receivers are not complete with large portions of data gaps. The processing to obtain complete TEC maps for space science research is time consuming and needs the collaboration of five International GNSS Service (IGS) Ionosphere Associate Analysis Centers (IAACs) to use different data processing and filling algorithms and to consolidate their results into final IGS completed TEC maps. In this work, we developed a Deep Convolutional Generative Adversarial Network (DCGAN) and Poisson blending model (DCGAN-PB) to learn IGS completion process for automatic completion of TEC maps. Using 10-fold cross validation of 20-year IGS TEC data, DCGAN-PB achieves the average root mean squared error (RMSE) about 4 absolute TEC units (TECu) of the high solar activity years and around 2 TECu for low solar activity years, which is about 50% reduction of RMSE for recovered TEC values compared to two conventional single-image inpainting methods. The developed DCGAN-PB model can lead to an efficient automatic completion tool for TEC maps.</p>

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