Terahertz (THz) sensing is a promising imaging technology for a wide variety of different applications. Extracting the interpretable and physically meaningful parameters for such applications, however, requires solving an inverse problem in which a model function determined by these parameters needs to be fitted to the measured data. Since the underlying optimization problem is nonconvex and very costly to solve, we propose learning the prediction of suitable parameters from the measured data directly. More precisely, we develop a model-based autoencoder in which the encoder network predicts suitable parameters and the decoder is fixed to a physically meaningful model function, such that we can train the encoding network in an unsupervised way. We illustrate numerically that the resulting network is more than 140 times faster than classical optimization techniques while making predictions with only slightly higher objective values. Using such predictions as starting points of local optimization techniques allows us to converge to better local minima about twice as fast as optimization without the network-based initialization.
Finally, a recent work has exploited deep learning techniques in Terahertz imaging in @cite_20 , but the considered application of super-resolving the THz amplitude image @math by training a convolutional neural network on synthetically blurred images is not directly related to our proposed approach.
@cite_20
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We propose an effective and robust method for terahertz (THz) image super-resolution based on a deep convolutional neural network (CNN). A deep CNN model is designed. It learns an end-to-end mapping between the low- and high-resolution images. Blur kernels with multiple width and noise with multiple levels are taken into the training set so that the network can handle THz images very well. Quantitative comparison of the proposed method and other super-resolution methods on the synthetic THz images indicates that the proposed method performs better than other methods in accuracy and visual improvements. Experimental results on real THz images show that the proposed method significantly improves the quality of THz images with increased resolution and decreased noise, which proves the practicability and exactitude of the proposed method.