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Many successful machine learning methods have been developed for microwaveinversion problems. However, so far, their inversion has been performed only atthe specifically trained frequencies. To make the machine-learning-basedinversion method more generalizability for realistic engineering applications,this work proposes a residual fully convolutional network (Res-FCN) to performmicrowave inversion of high contrast scatterers at an arbitrary frequencywithin a wide frequency band. The proposed Res-FCN combines the advantages ofthe Res-Net and the fully convolutional network (FCN). Res-FCN consists of anencoder and a decoder: the encoder is employed to extract high-dimensionalfeatures from the measured scattered field through the residual frameworks,while the decoder is employed to map from the high-dimensional featuresextracted by the encoder to the electrical parameter distribution in theinversion region by the up-sample layer and the residual frameworks. Fivenumerical examples verify that the proposed Res-FCN can achieve goodperformance in the 2-D microwave inversion problem for high contrast scattererswith anti-noise ability at an arbitrary frequency point within a wide frequencyband.

A Res-FCN for Electromagnetic Inversion of High Contrast Scatterers at an Arbitrary Frequency Within a Wide Frequency Band