Multi-frequency Neural Born Iterative Method for Solving 2-D Inverse Scattering Problems
Daoqi Liu, Tao Shan, Maokun Li, Fan Yang, Shenheng Xu

TL;DR
This paper introduces a deep learning-based multi-frequency neural Born iterative method that improves accuracy and efficiency in solving 2-D electromagnetic inverse scattering problems by integrating physical laws and multitask learning.
Contribution
It develops a novel multi-frequency neural Born iterative method combining multitask learning and physics-guided unsupervised training, enhancing robustness and generalization in electromagnetic inverse scattering.
Findings
Improved accuracy in synthetic and experimental data
Enhanced computational efficiency over traditional methods
Strong noise resistance and generalization capabilities
Abstract
In this work, we propose a deep learning-based imaging method for addressing the multi-frequency electromagnetic (EM) inverse scattering problem (ISP). By combining deep learning technology with EM physical laws, we have successfully developed a multi-frequency neural Born iterative method (NeuralBIM), guided by the principles of the single-frequency NeuralBIM. This method integrates multitask learning techniques with NeuralBIM's efficient iterative inversion process to construct a robust multi-frequency Born iterative inversion model. During training, the model employs a multitask learning approach guided by homoscedastic uncertainty to adaptively allocate the weights of each frequency's data. Additionally, an unsupervised learning method, constrained by the physical laws of ISP, is used to train the multi-frequency NeuralBIM model, eliminating the need for contrast and total field…
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Taxonomy
TopicsMicrowave Imaging and Scattering Analysis · Numerical methods in inverse problems · Image and Signal Denoising Methods
