A Physics-Assisted Deep Learning Microwave Imaging Framework for Real-Time Shape Reconstruction of Unknown Targets

Abstract

In this paper an innovative approach to microwave imaging, which combines a qualitative imaging technique and deep learning, is presented. The goal is to develop a tool for reliable and user-independent retrieval of the shape of unknown targets from the knowledge of the scattered fields. Qualitative imaging methods are powerful inverse scattering tools, as they provide morphological information in real-time. However, their outcome is a continuous map which has to be hard-thresholded to clearly identify the targets. This thresholding unavoidably results in case-dependent, often user-biased, results. To deal with this issue, a deep learning approach, based on a physics-assisted deep neural network is proposed to automatically classify image pixels, i.e., to generate binary masks, separating the targets (foreground) from the background. In particular, the proposed network binarizes the output of a qualitative imaging inversion technique known as orthogonality sampling method. For the sake of comparison, a deep learning method is also exploited, which generates the binary masks directly from the scattered fields without any qualitative imaging aid. A quantitative assessment of the performances of both methods as well as a test on experimental data are provided.

Experimental Assessment of Real-Time Brain Stroke Monitoring via a Microwave Imaging Scanner

“…In addition, substituting actual electromechanical switches with solid-state ones will improve the device performance by decreasing acquisition times by more than 10x , reaching scan times in the order of seconds [16]. Moreover, we plan to further enhance the resolution of the imaging using physics-assisted deep learning algorithms as in [43]. Finally, the authors will investigate novel calibration techniques, such as the one presented in [44], in order to improve the match between the real data and the EM numerical model; in fact, it is well known that any difference between them may affect the imaging operator reliability, thus the image quality.…”

A deep learning enhanced inverse scattering framework for microwave imaging of piece-wise homogeneous targets

“…• Different from our previous works where U-Net task consisted in classification problems (binary segmentation [18] or categorical segmentation [19]), the U-Net is herein trained within a pixel-wise regression framework, to allow retrieving a continuous set of values; • The a priori information on the piece-wise nature of the targets is encoded by representing the spatial map of the EM properties distribution to be predicted by the network in terms of the corresponding spatial gradient, which allows to explicitly enforce into the training process the implicitly sparse nature of the information to be retrieved. We refer to this map as the augmented shape, to recall that it conveys information on both the target's internal and external boundaries and the relative contrast variation with respect to the (known) background medium;…”

“…Motivated by the above considerations, the authors of this work have considered the use of the orthogonality sampling method (OSM) [17] as the domain knowledge-embedding imaging algorithm [18,19]. The OSM is a qualitative method introduced by Roland Potthast, in which an indicator function is computed to estimate the shape of the unknown targets.…”

“…Last but not least, it has been shown in [20] that OSM images encode information on the spatial behavior of the EM properties of the targets, owing to the relation between the OSM indicator and the radiating component of the induced currents. Based on these considerations, it was shown that a U-Net fed with OSM images could be trained to achieve an objective reconstruction of the targets' shape [18] or an estimate of the targets shape and EM properties, provided they belonged to a fixed and known set of values [19].…”

Deep Neural Network-Oriented Indicator Method for Inverse Scattering Problems Using Partial Data

“…Although the deep learning methods can process partial data, the performance is highly problem-dependent and the network structures are often sophisticated. Combinations of deep learning and sampling methods for the inverse scattering problems have also been investigated recently [13][14][15][16]. These methods usually construct a DNN to learn the reconstruction of some sampling method.…”