A deep learning-based approach in conjugation with Fourier Diffraction Theorem (FDT) is proposed in this paper to solve the inverse scattering problem arising in microwave imaging. The proposed methodology is adept in generating a permittivity mapping of the object in less than a second and hence has the potential for real-time imaging. The reconstruction of the dielectric permittivity from the measured scattered field values is done in a single step as against that by a long iterative procedure employed by conventional numerical methods. The proposed technique proceeds in two stages; with the initial estimate of the contrast function being generated by the FDT in the first stage. This initial profile is fed to a trained U-net to reconstruct the final dielectric permittivities of the scatterer in the second stage. The capability of the proposed method is compared with other works in the recent literature using the Root Mean Square Error (RMSE). The proposed method generates an RMSE of 0.0672 in comparison to similar deep learning methods like Back Propagation-Direct Sampling Method (BP-DSM) and Subspace-Based Variational Born Iterative Method (SVBIM), which produce error values 0.1070 and 0.0813 in the case of simulation (using Austria Profile). The RMSE level while reconstructing the experimental data (FoamDielExt experimental database) is 0.0922 for the proposed method as against 0.1631 and 0.1037 for BP-DSM and SVBIM, respectively.
2. Anjit, T. A., R. Benny, P. Cherian, and P. Mythili, "Non-iterative microwave imaging solutions for inverse problems using deep learning," Progress In Electromagnetics Research M, Vol. 102, 53-63, 2021.
3. Wang, F., et al., "Multi-resolution convolutional neural networks for inverse problems," Scientific Reports, Vol. 10, 1-11, 2020.
4. Khoshdel, V., A. Ashraf, and J. LoVetri, "Enhancement of multimodal microwave-ultrasound breast imaging using a deep-learning technique," Sensors, Vol. 4050, 1-14, 2019.
5. Wei, Z. and X. Chen, "Deep-learning schemes for full-wave nonlinear inverse scattering problems," IEEE Trans. Geosci. Remote Sens., Vol. 57, 1849-1860, 2019.
6. Yao, H. M., W. E. I. Sha, and L. Jiang, "Two-step enhanced deep learning approach for electromagnetic inverse scattering problems," IEEE Antennas and Wireless Propagation Letters, Vol. 18, 2254-2258, 2019.
7. Jin, K. H., M. T. McCann, E. Froustey, and M. Unser, "Deep convolutional neural network for inverse problems in imaging," IEEE Trans. Image Processing, Vol. 26, 4509-4522, 2017.
8. Zhang, L., K. Xu, R. Song, X. Z. Ye, G. Wang, and X. Chen, "Learning-based quantitative microwave imaging with a hybrid input scheme," IEEE Sensors Journal, Vol. 20, 15007-15013, 2020, doi: 10.1109/JSEN.2020.3012177.
9. Kak, A. C. and M. Slaney, Principles of Computerized Tomographic Imaging, Society of Industrial and Applied Mathematics, July 2001.
10. Deng, L., "The MNIST database of handwritten digit images for machine learning research," IEEE Signal Processing Magazine, Vol. 29, 141-142, 2012, doi:10.1109/MSP.2012.2211477.
11. Geffrin, J.-M., P. Sabouroux, and C. Eyraoud, "Free space experimental scattering database continuation: Experimental set-up and measurement precision," Inverse Probl., Vol. 21, 117-130, 2005.
12. Li, L., et al., "DeepNIS: Deep neural network for nonlinear electromagnetic inverse scattering," IEEE Trans. Antennas and Propag., Vol. 67, 1819-1825, 2019.