Multi-Frequency Progressive Refinement for Learned Inverse Scattering
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arXiv
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| Autori principali: | , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2024
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| _version_ | 1866917973318434816 |
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| author | Melia, Owen Tsang, Olivia Charisopoulos, Vasileios Khoo, Yuehaw Hoskins, Jeremy Willett, Rebecca |
| author_facet | Melia, Owen Tsang, Olivia Charisopoulos, Vasileios Khoo, Yuehaw Hoskins, Jeremy Willett, Rebecca |
| contents | Interpreting scattered acoustic and electromagnetic wave patterns is a computational task that enables remote imaging in a number of important applications, including medical imaging, geophysical exploration, sonar and radar detection, and nondestructive testing of materials. However, accurately and stably recovering an inhomogeneous medium from far-field scattered wave measurements is a computationally difficult problem, due to the nonlinear and non-local nature of the forward scattering process. We design a neural network, called Multi-Frequency Inverse Scattering Network (MFISNet), and a training method to approximate the inverse map from far-field scattered wave measurements at multiple frequencies. We consider three variants of MFISNet, with the strongest performing variant inspired by the recursive linearization method--a commonly used technique for stably inverting scattered wavefield data--that progressively refines the estimate with higher frequency content. MFISNet outperforms past methods in regimes with high-contrast, heterogeneous large objects, and inhomogeneous unknown backgrounds. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2405_13214 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Multi-Frequency Progressive Refinement for Learned Inverse Scattering Melia, Owen Tsang, Olivia Charisopoulos, Vasileios Khoo, Yuehaw Hoskins, Jeremy Willett, Rebecca Computational Physics Interpreting scattered acoustic and electromagnetic wave patterns is a computational task that enables remote imaging in a number of important applications, including medical imaging, geophysical exploration, sonar and radar detection, and nondestructive testing of materials. However, accurately and stably recovering an inhomogeneous medium from far-field scattered wave measurements is a computationally difficult problem, due to the nonlinear and non-local nature of the forward scattering process. We design a neural network, called Multi-Frequency Inverse Scattering Network (MFISNet), and a training method to approximate the inverse map from far-field scattered wave measurements at multiple frequencies. We consider three variants of MFISNet, with the strongest performing variant inspired by the recursive linearization method--a commonly used technique for stably inverting scattered wavefield data--that progressively refines the estimate with higher frequency content. MFISNet outperforms past methods in regimes with high-contrast, heterogeneous large objects, and inhomogeneous unknown backgrounds. |
| title | Multi-Frequency Progressive Refinement for Learned Inverse Scattering |
| topic | Computational Physics |
| url | https://arxiv.org/abs/2405.13214 |