Deep Learning Approach for Microwave Imaging Based on Deep Convolutional Asymmetric Encoder-Decoder Structure and Physics-Induced Loss

In this paper, we introduce an innovative deep learning (DL) methodology designed for real-time quantitative microwave imaging (MWI). Our approach is centered around the utilization of a deep convolutional asymmetric encoder-decoder structure (DCAEDS), which requires only a single-frequency far-field measurement of the electromagnetic (EM) scattered field as input and subsequently predicts the contrasts (permittivities) of the target materials. During the offline training process, we incorporate an EM forward solver specifically crafted to compute the EM scattered field generated by the predicted target contrasts (permittivities) produced by the DCAEDS. The DCAEDS is seamlessly integrated with this EM forward solver to optimize the loss function. This loss function comprises two fundamental components: (1) Data-induced loss, directly quantifying the dissimilarity between the predictions of our proposed DCAEDS and the actual labels for the target contrasts (permittivities); (2) Physics-induced loss, evaluating the distinctions between the measured EM scattered field and the computed EM scattered field derived from the predicted target contrasts (permittivities) generated by the DCAEDS. Our DL approach excels in delivering precise results, even for high-contrast targets, overcoming limitations associated with conventional methods, such as computational cost and ill-conditioning. Numerical benchmarks using dielectric targets underscore the practicality and effectiveness of our DL-based approach.