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GAN-based Monochromatic Full-Focus THz Imaging

The volumetric imaging based on a monochromatic imaging system suffers from serious defocusing due to its limited depth-of-focus. To address this problem, we propose a monochromatic full-focus terahertz (THz) imaging method from the image processing point of view. It is an end-to-end learning method to focus volume target whose echo is received by a two-dimensional THz synthetic aperture radar operating at a single frequency and without bandwidth character. The learning is based on a generative adversarial network (GAN). In order to make THz reconstructed images suitable for GAN, we use grayscale and pseudo-color preprocessing for dataset preparation respectively. Results demonstrate that this method can greatly improve the focusing performance of monochromatic THz imaging system. In specific, compared with the grayscale-image-based training, the training based on pseudo-color THz images can make the output of full-focus GAN model achieve better performance with respect to the peak signal-to-noise ratio, the structural similarity measure and visual appearance.