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2021-04-13
Non-Iterative Microwave Imaging Solutions for Inverse Problems Using Deep Learning
By
Progress In Electromagnetics Research M, Vol. 102, 53-63, 2021
Abstract
This paper describes a U-net based Deep Learning (DL) approach in combination with Subspace-Based Variational Born Iterative Method (SVBIM) to provide a solution for quantitative reconstruction of scatterer from the measured scattered field. The proposed technique can be used as an alternative to conventional time consuming and computationally complex iterative methods. This technique comprises of a numerical solver (SVBIM) for generating the initial contrast function and a DL network to reconstruct the scatterer profile from the initial contrast function. Further, the proposed technique is validated against theoretical and experimental results available from the literature. Root Mean Square Error (RMSE) value is used as the metric to measure the accuracy of the reconstructed image. The RMSE values of the proposed method show a significant reduction in the reconstruction error when compared with the recent Back Propagation-Direct Sampling Method (BP-DSM). The proposed method produces an RMSE value of 0.0813 against 0.1070 in the case of simulation (Austria Profile). The error value obtained by validating against the FoamDielExt experimental database in the case of the proposed method is 0.1037 against 0.1631 reported for BP-DSM method.
Citation
Thathamkulam Anjit, Ria Benny, Philip Cherian, and Palayyan Mythili, "Non-Iterative Microwave Imaging Solutions for Inverse Problems Using Deep Learning," Progress In Electromagnetics Research M, Vol. 102, 53-63, 2021.
doi:10.2528/PIERM21021304
References

1. Chandra, R., H. Zhou, I. Balasingham, and R. M. Narayanan, "On the opportunities and challenges in microwave medical sensing and imaging," IEEE Transactions on Biomedical Engineering, Vol. 62, No. 7, 1667-1682, 2015.
doi:10.1109/TBME.2015.2432137

2. Ambrosanio, M., P. Kosmas, and V. Pascazio, "A multithreshold iterative DBIM-based algorithm for the imaging of heterogeneous breast tissues," IEEE Transactions on Biomedical Engineering, Vol. 66, No. 2, 509-520, 2018.
doi:10.1109/TBME.2018.2849648

3. Chen, X., Computational Methods for Electromagnetic Inverse Scattering, Wiley, Hoboken, NJ, USA, 2018.
doi:10.1002/9781119311997

4. Randazzo, A., C. Ponti, A. Fedeli, C. Estatico, P. D'Atanasio, M. Pastorino, and G. Schettini, "A two-step inverse-scattering technique in variable-exponent lebesgue spaces for through-the-wall microwave imaging: Experimental results," IEEE Transactions on Geoscience and Remote Sensing, 2021.

5. Huang, T. and A. S. Mohan, "Microwave imaging of perfect electrically conducting cylinder by micro-genetic algorithm," IEEE Antennas and Propagation Society Symposium, Vol. 1, IEEE, 2004.

6. Semenov, S. Y., et al. "Microwave-tomographic imaging of the high dielectric-contrast objects using different image-reconstruction approaches," IEEE Trans. Microw. Theory Tech., Vol. 53, No. 7, 2284-2294, Jul. 2005.
doi:10.1109/TMTT.2005.850459

7. Rajan, S. D. and G. V. Frisk, "A comparison between the Born and Rytov approximations for the inverse backscattering problem," Geophysics, Vol. 54, 864-871, 1989.
doi:10.1190/1.1442715

8. Majobi, P. and J. LeVetri, "Comparison of TE and TM inversions in the framework of the Gauss-Newton Method," IEEE Transactions on Antennas and Propagation, Vol. 64, 1336-1348, 2010.
doi:10.1109/TAP.2010.2041156

9. Rocca, P., M. Benedetti, M. Donelli, D. Franceschini, and A. Massa, "Evolutionary optimization as applied to inverse problems," Inverse Probl., Vol. 25, 1-41, 2009.

10. Candes, E. J. and M. B. Wakin, "An introduction to compressive sampling," IEEE Signal Processing Magazine, Vol. 25, No. 2, 21-30, 2008.
doi:10.1109/MSP.2007.914731

11. Lustig, M., D. Donoho, and J. M. Pauly, "Sparse MRI: The application of compressed sensing for rapid MR imaging," Magnetic Resonance in Medicine, Vol. 58, No. 6, 1182-1195, 2008.
doi:10.1002/mrm.21391

12. Pan, X. and E. Y. Sidky, "Image reconstruction in circular cone-beam computed tomography by constrained, total-variation minimization," Physics in Medicine and Biology, Vol. 53, No. 17, 4777-4807, 2008.
doi:10.1088/0031-9155/53/17/021

13. Naghsh, N. Z., A. Ghorbani, and H. Amindavar, "Compressive sensing for microwave breast cancer imaging," IET Signal Processing, Vol. 12, No. 2, 242-246, 2017.
doi:10.1049/iet-spr.2015.0537

14. Rekanos, I. T., "Neural-network-based inverse-scattering technique for online microwave medical imaging," IEEE Transactions on Magnetics, Vol. 38, No. 2, 1061-1064, 2002.
doi:10.1109/20.996272

15. Wei, Z. and X. Chen, "Deep-learning schemes for full-wave nonlinear inverse scattering problems," IEEE Transactions on Geoscience and Remote Sensing, Vol. 57, No. 4, 1849-1860, 2019.
doi:10.1109/TGRS.2018.2869221

16. Guo, R., X. Song, M. Li, F. Yang, S. Xu, and A. Abubakar, "Supervised descent learning technique for 2-D microwave imaging," IEEE Transactions on Antennas and Propagation, Vol. 67, No. 5, 3550-3554, 2019.
doi:10.1109/TAP.2019.2902667

17. 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, No. 24, 15007-15013, 2020.
doi:10.1109/JSEN.2020.3012177

18. Geffrin, J.-M., P. Sabouroux, and C. Eyraoud, "Free space experimental scattering database continuation: Experimental set-up and measurement precision," Inverse Probl., Vol. 21, No. 6, 117-130, 2005.
doi:10.1088/0266-5611/21/6/S09

19. Chew, W. C. and Y. M. Wang, "Reconstruction of two-dimensional permittivity distribution using the distorted Born iterative method," IEEE Trans. Medical. Imag., Vol. 9, No. 2, 218-225, 1990.
doi:10.1109/42.56334

20. Anzengruber, S. W. and R. Ramlau, "Convergence rates for morozov’s discrepancy principle using variational inequalities," Inverse Problems, Vol. 27, No. 10, 105007, 2011.
doi:10.1088/0266-5611/27/10/105007

21. Liu, Z. and Z. Nie, "Subspace-based variational born iterative method for solving inverse scattering problems," IEEE Geoscience and Remote Sensing Letters, Vol. 16, No. 7, 1017-1020, Jul. 2019.
doi:10.1109/LGRS.2018.2889886

22. Li, M., O. Semerci, and A. Abubakar, "A contrast source inversion method in the wavelet domain," Inverse Probl., Vol. 29, No. 2, 025015, 2013.
doi:10.1088/0266-5611/29/2/025015

23. Ye, X., X. Chen, Y. Zhong, and K. Agarwal, "Subspace-based optimization method for reconstructing perfectly electric conductors," Progress In Electromagnetic Research, Vol. 100, 119-128, 2010.
doi:10.2528/PIER09111606

24. Zhong, Y. and X. Chen, "An FFT twofold subspace-based optimization method for solving electromagnetic inverse scattering problems," IEEE Transactions on Antennas and Propagation, Vol. 59, No. 3, 914-927, 2011.
doi:10.1109/TAP.2010.2103027

25. Ronneberger, O., P. Fischer, and T. Brox, "U-net: Convolutional networks for biomedical image segmentation," Proc. 18th Int. Conf. Med. Image Comput. Comput.-Assist. Intervention, 234-241, 2015.

26. 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, No. 11, 2254-2258, Nov. 2019.
doi:10.1109/LAWP.2019.2925578

27. Xu, K., L. Wu, X. Ye, and X. Chen, "Deep learning-based inversion methods for solving inverse scattering problems with phaseless data," IEEE Transactions on Antennas and Propagation, Vol. 68, No. 11, 7457-7470, 2020.
doi:10.1109/TAP.2020.2998171

28. Zhang, Z., "Improved adam optimizer for deep neural networks," 2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS), 1-2, Banff, AB, Canada, 2018.

29. Rahangdale, A. and S. Raut, "Deep neural network regularization for feature selection in learning-to-rank," IEEE Access, Vol. 7, 53988-54006, 2019.
doi:10.1109/ACCESS.2019.2902640

30. Deng, L., "The MNIST database of handwritten digit images for machine learning research [Best of the Web]," IEEE Signal Processing Magazine, Vol. 29, No. 6, 141-142, Nov. 2012.
doi:10.1109/MSP.2012.2211477

31. Azghani, M. and F. Marvasti, "L2-regularized iterative weighted algorithm for inverse scattering," IEEE Transactions on Antennas and Propagation, Vol. 64, No. 6, 2293-2300, 2016.
doi:10.1109/TAP.2016.2546385