Embedding Deep Learning in Inverse Scattering Problems
| Autor: | Yaswanth Kalepu, Yash Sanghvi, Uday K. Khankhoje |
|---|---|
| Rok vydání: | 2020 |
| Předmět: |
Iterative method
business.industry Deep learning 020206 networking & telecommunications 02 engineering and technology 010501 environmental sciences Inverse problem 01 natural sciences Convolutional neural network Computer Science Applications Maxima and minima Computational Mathematics Signal Processing Convergence (routing) Inverse scattering problem 0202 electrical engineering electronic engineering information engineering Embedding Artificial intelligence business Algorithm 0105 earth and related environmental sciences |
| Zdroj: | IEEE Transactions on Computational Imaging. 6:46-56 |
| ISSN: | 2334-0118 2573-0436 |
| DOI: | 10.1109/tci.2019.2915580 |
| Popis: | In this paper, we introduce a deep-learning-based framework to solve electromagnetic inverse scattering problems. This framework builds on and extends the capabilities of existing physics-based inversion algorithms. These algorithms, such as the contrast source inversion, subspace-optimization method, and their variants face a problem of getting trapped in false local minima when recovering objects with high permittivity. We propose a novel convolutional neural network architecture, termed the contrast source network, that learns the noise space components of the radiation operator. Together with the signal space components directly estimated from the data, we iteratively refine the solution and show convergence to the correct solution in cases where traditional techniques fail without any significant increase in computational time. We also propose a novel multiresolution strategy that helps in producing high resolution solutions without any significant increase in computational costs. Through extensive numerical experiments, we demonstrate the ability to recover high permittivity objects that include homogeneous, heterogeneous, and lossy scatterers. |
| Databáze: | OpenAIRE |
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