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Multi-Frequency Neural Born Iterative Method for Solving 2-D Inverse Scattering Problems
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DOI：10.1109/TCI.2025.3607150.png)
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Abstract
En 中文
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 computation, 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 physics of the ISP, is used to train the multi-frequency NeuralBIM model, eliminating the need for contrast and total field data. The effectiveness of the multi-frequency NeuralBIM is validated through synthetic and experimental data, demonstrating improvements in accuracy and computational efficiency for solving the ISP. Moreover, this method exhibits good generalization capabilities and noise resistance.
Keywords:
Iterative methods
Inverse problems
Deep learning
Imaging
Training
Image reconstruction
Convolutional neural networks
Physics
Mathematical models
Computational modeling
Born iterative method
inverse scattering problem (ISP)
deep learning
multitask learning
unsupervised learning
Journal
I
IF:
4.8
Papers:
128
Citations:
0