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