Microwave imaging (MWI) is a non-invasive technique that can identify unknown scatterer objects’ features while offering advantages such as low cost and portable devices with respect to other imaging methods. However, MWI faces challenges in solving the underlying inverse scattering problem, which involves recovering target properties from its scattered fields. Existing methods include linearized and non-linear optimization approaches, but they have limitations respectively in terms of range of validity and computational complexity (in view of the possible occurrence of ‘false solutions’). In recent years, learning-based approaches have emerged as they can allow real-time imaging but usually lack generalizability and a direct connection to the underlying physics. This paper proposes a physics-informed approach that combines convolutional neural networks with physics-based calculations. It is based on a few cascaded operations, making use of the gradient of the relevant cost function, and successively improving the estimation of the unknown target. The proposed approach is assessed using simulated as well as experimental Fresnel data. The results show that the integration of physics with deep learning can contribute to improve reconstruction accuracy, generalizability, and computational efficiency in MWI.

中文翻译：

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MiPhDUO：通过基于物理的深度展开优化进行微波成像

微波成像（MWI）是一种非侵入性技术，可以识别未知散射体的特征，同时相对于其他成像方法具有低成本和便携式设备等优势。然而，MWI 在解决潜在的逆散射问题方面面临着挑战，其中涉及从散射场恢复目标属性。现有的方法包括线性优化方法和非线性优化方法，但它们分别在有效性范围和计算复杂性方面存在局限性（考虑到可能出现“错误解”）。近年来，基于学习的方法出现了，因为它们可以实现实时成像，但通常缺乏通用性以及与基础物理的直接联系。本文提出了一种基于物理的方法，将卷积神经网络与基于物理的计算相结合。它基于一些级联操作，利用相关成本函数的梯度，逐步改进对未知目标的估计。使用模拟和实验菲涅耳数据对所提出的方法进行评估。结果表明，物理学与深度学习的结合有助于提高 MWI 的重建精度、通用性和计算效率。