In electromagnetic inverse scattering, we aim to reconstruct object permittivity from scattered waves. Deep learning is a promising alternative to traditional iterative solvers, but it has been used mostly in a supervised framework to regress the permittivity patterns from scattered fields or back-projections. While such methods are fast at test-time and achieve good results for specific data distributions, they are sensitive to the distribution drift of the scattered fields, common in practice. If the distribution of the scattered fields changes due to changes in frequency, the number of transmitters and receivers, or any other real-world factor, an end-to-end neural network must be re-trained or fine-tuned on a new dataset. In this paper, we propose a new data-driven framework for inverse scattering based on deep generative models. We model the target permittivities by a low-dimensional manifold which acts as a regularizer and learned from data. Unlike supervised methods which require both scattered fields and target signals, we only need the target permittivities for training; it can then be used with any experimental setup. We show that the proposed framework significantly outperforms the traditional iterative methods especially for strong scatterers while having comparable reconstruction quality to state-of-the-art deep learning methods like U-Net.
翻译:在电磁反散射中,我们的目标是从分散的波浪中重建物体允许性。深层学习是传统迭代解答器的一个很有希望的替代物,但大多是在监督的框架内用来从分散的字段或反向分散的场或反向投射。虽然这些方法在测试时速度很快,在特定数据分布方面取得了良好的结果,但它们对分散的场的分布变化很敏感,这是惯例上常见的。如果分散的场的分布由于频率的变化而变化,发射机和接收机的数量,或任何其他现实世界因素而变化,一个端到端的神经网络必须经过重新培训或微调,在新的数据集中必须加以调整。在本文件中,我们提出了一个新的数据驱动框架,以便根据深层基因化模型反向分散。我们用一个低维的外径模型来模拟目标许可性,该模型的作用是正常的,从数据学。与监督的方法不同,既需要分散的场和目标信号,我们只需要目标许可性的方法;然后可以对任何实验性设置加以使用。我们表明,拟议的框架在深度质量上大大超越了传统的迭基方法,同时学习了传统的模拟方法。我们展示了类似的深度结构。