On the computational complexity of the conjugate-gradient method for solving inverse scattering problems

Vargas, Jose O.; Batista, André Costa; Batista, Lucas S.; Adriano, Ricardo

doi:10.1080/09205071.2021.1946862

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Dbim Assisted Deep Learning Scheme1

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2022

2024

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Cited by 5 publications

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“…In addition, a Tikhonov regularization term is added to the cost function to stabilize the optimization procedure. The cost function is defined as [24] δ χn = arg min δχ…”

Section: Dbim Assisted Deep Learning Schemementioning

confidence: 99%

Deep Learning Assisted Distorted Born Iterative Method for Solving Electromagnetic Inverse Scattering Problems

Shimoga Beerappa,

Erramshetty,

Magdum

2023

PIER C

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This paper presents the deep learning-assisted Distorted Born Iterative Method (DBIM) for the permittivity reconstruction of dielectric objects. The inefficiency of DBIM to reconstruct strong scatterers can be overcome if it is supported by Convolutional Neural Network (CNN). A novel approach, cascaded CNN is used to obtain a fine-resolution estimate of the permittivity distribution. The CNN is trained using images consisting of MNIST digits, letters, and circular objects. The proposed model is tested on synthetic data with different signal-to-noise ratios (SNRs) and various contrast profiles. Thereafter, it is verified through experimental data provided by the Institute of Fresnel, France. Reconstruction results show that the proposed inversion method outperforms the conventional DBIM method in terms of accuracy and convergence rate.

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“…In addition, a Tikhonov regularization term is added to the cost function to stabilize the optimization procedure. The cost function is defined as [24] δ χn = arg min δχ…”

Section: Dbim Assisted Deep Learning Schemementioning

confidence: 99%