Hostname: page-component-76fb5796d-vfjqv Total loading time: 0 Render date: 2024-04-26T22:37:22.451Z Has data issue: false hasContentIssue false
  • English
  • Français

Distorted Born iterative method with back-propagation improves permittivity reconstruction

Published online by Cambridge University Press:  28 April 2022

Amit Magdum Open the ORCID record for Amit Magdum [Opens in a new window]  and
Mallikarjun Erramshetty
Amit Magdum*
Affiliation:
Department of Electronics and Communication Engineering, National Institute of Technology Goa, Ponda, India
Mallikarjun Erramshetty
Affiliation:
Department of Electronics and Communication Engineering, National Institute of Technology Goa, Ponda, India
*
Author for correspondence: Amit Magdum, E-mail: amitmagdum7671@gmail.com
Get access Rights & Permissions [Opens in a new window]

Abstract

The distorted Born iterative method (DBIM) is a widely used quantitative reconstruction algorithm to solve the inverse scattering problems of microwave imaging. The major mathematical challenges in solving such problems are non-uniqueness, non-linearity, and ill-posedness. Due to these issues, the optimization algorithm converges to a local minimum. This drawback can be overcome by selecting the correct initial guess solution, which helps to escape local minima and thus guides the inversion algorithm to a satisfactory result. This study uses a back-propagation algorithm to calculate the initial estimate, which significantly accelerates the rate of convergence and improves the accuracy of the standard DBIM approach. The results of this method are compared with zero initialization and Born-approximated initialization. For comparison, weak as well as strong scattering profiles of synthetic and experimental dataset are considered. The results suggest that the proposed method provides a significant improvement in terms of computing cost and efficiency. Furthermore, the proposed technique has the potential to successfully push the limits of reconstructible contrast.

Keywords

Back-propagationBorn approximationdistorted Born iterative methodinitial guess solutionmicrowave imaging
Type
EM Field Theory
Information
International Journal of Microwave and Wireless Technologies , Volume 15 , Issue 2 , March 2023 , pp. 331 - 337
Check for updates
Copyright
Copyright © The Author(s), 2022. Published by Cambridge University Press in association with the European Microwave Association

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

Pastorino, M (2010) Microwave Imaging. Hoboken: John Wiley & Sons.CrossRefGoogle Scholar
Boero, F, Fedeli, A, Lanini, M, Maffongelli, M, Monleone, R, Pastorino, M, Randazzo, A, Salvade, A and Sansalone, A (2018) Microwave tomography for the inspection of wood materials: imaging system and experimental results. IEEE Transactions on Microwave Theory and Techniques, 66, 3497–3510.CrossRefGoogle Scholar
Ibrahim, A, Crocco, L, Amelie, L and Ali, Y (2015) Progress in microwave imaging: from theoretical developments to cutting-edge applications. International Journal of Antennas and Propagation, 2015, 1–2.Google Scholar
Chen, X (2018) Computational methods for electromagnetic inverse scattering. Singapore: Wiley-IEEE Press.CrossRefGoogle Scholar
Zhong, Y, Lambert, M, Lesselier, D and Chen, X (2016) A new integral equation method to solve highly nonlinear inverse scattering problems. IEEE Transactions on Antennas and Propagation, 64, 1788–1799.CrossRefGoogle Scholar
Chew, W (1995) Waves and Fields in Inhomogeneous Media. New York: Wiley-IEEE Press).Google Scholar
Caorsi, S and Pastorino, M (2000) Two-dimensional microwave imaging approach based on a genetic algorithm. IEEE Transactions on Antennas and Propagation, 48, 370–373.CrossRefGoogle Scholar
Dorigo, M, Birattari, M and Stutzle, T (2006) Ant colony optimization. IEEE Computational Intelligence Magazine, 1, 28–39.CrossRefGoogle Scholar
Chakraborty, UK (2008) Advances in Differential Evolution. New York: Springer.CrossRefGoogle Scholar
Salucci, M, Poli, L, Anselmi, N and Massa, A (2017) Multifrequency particle swarm optimization for enhanced multiresolution GPR microwave imaging. IEEE Transactions on Geoscience and Remote Sensing, 55, 1305–1317.CrossRefGoogle Scholar
Berg, P and Kleinman, R (1997) A contrast source inversion method. Inverse Problems, 13, 1607–1620.CrossRefGoogle Scholar
Benedetto, F, Estatico, C, Nagy, J and Pastorino, M (2009) Numerical linear algebra for nonlinear microwave imaging. Electronic Transactions on Numerical Analysis, 33, 105–125.Google Scholar
Kleinman, RE and Berg, PM (1992) A modified gradient method for two-dimensional problems in tomography. Journal of Computational and Applied Mathematics, 42, 17–35.CrossRefGoogle Scholar
Jun, SC and Choi, UJ (2007) Convergence analyses of the born iterative method and the distorted born iterative method. Numerical Functional Analysis and Optimization, 20, 301–316.CrossRefGoogle Scholar
Ye, X and Chen, X (2017) Subspace-based distorted-Born iterative method for solving inverse scattering problems. IEEE Transactions on Antennas and Propagation, 65, 7224–7232.CrossRefGoogle Scholar
Chew, WC and Wang, YM (1990) Reconstruction of two-dimensional permittivity distribution using the distorted Born iterative method. IEEE Transactions on Medical Imaging, 9, 218–225.CrossRefGoogle ScholarPubMed
Liu, H and Ye, X, Reconstruction of Dielectric Parameters of Human Tissues Using Distorted Born Iterative Method, 2019 IEEE International Conference on Computational Electromagnetics (ICCEM), Shanghai, China, 2019.CrossRefGoogle Scholar
Abubakar, A, Berg, P and Habashy, T (2006) An integral equation approach for 2.5-dimensional forward and inverse electromagnetic scattering. Geophysical Journal International, 165, 744–762.CrossRefGoogle Scholar
Kalepu, Y, Bhattacharya, S and Khankhoje, U, Algebraic Reconstruction Techniques for Inverse Imaging, International Conference on Electromagnetics in Advanced Applications (ICEAA), Cairns, QLD, Australia, 2016.Google Scholar
Chew, WC and Lin, J (1995) A frequency-hopping approach for microwave imaging of large inhomogeneous bodies. IEEE Microwave and Guided Wave Letters, 5, 439–441.CrossRefGoogle Scholar
Gordon, R, Bender, R and Herman, G (1970) Algebraic reconstruction techniques (ART) for three-dimensional electron microscopy and X-ray photography. Journal of theoretical Biology, 29, 471–481.CrossRefGoogle ScholarPubMed
Meng, ZQ, Takenaka, T and Tanaka, T (1999) Image reconstruction of two-dimensional impenetrable objects using genetic algorithm. Journal of Electromagnetic Waves and Applications, 13, 95–118.CrossRefGoogle Scholar
Belkebir, K and Saillard, M (2001) Special section: Testing inversion algorithms against experimental data. Inverse Problems, 17, 1565–1571.CrossRefGoogle Scholar
Geffrin, J, Sabouroux, P and Eyraud, C (2005) Free space experimental scattering database continuation: experimental set-up and measurement precision. Inverse Problems, 21, 117–130.CrossRefGoogle Scholar
Magdum, A, Erramshetty, M and Jagannath, R (2020) Regularized minimal residual method for permittivity reconstruction in microwave imaging. Microwave and Optical Technology Letters, 62, 1–13.CrossRefGoogle Scholar
Zhang, L, Xu, K, Song, R, Ye, X, Wang, G and Chen, X (2020) Learning-based quantitative microwave imaging with a hybrid input scheme. IEEE Sensors Journal, 20, 15007–15013.CrossRefGoogle Scholar
LeCun, Y, Bottou, L, Bengio, Y and Haffner, P (1998) Gradient-based learning applied to document recognition. Proceedings of the IEEE, 86, 2278–2324.CrossRefGoogle Scholar