Adaptive compressed sensing for the fast terahertz reflection tomography

In this paper, an adaptive compressed sensing is proposed in order to enhance the performance of fast tetrahertz reflection tomography. The proposed method first acquires data at random measurement points in the spatial domain, and estimates the regions in each tomographic image where much degradation is expected. Then, it allocates additional measurement points to those regions, so that more data are acquired adaptively at the regions prone to degradation, thereby improving the quality of the reconstructed tomographic images. The proposed method was applied to the T-ray reflection tomography system, and the image quality enhancement by the proposed method, compared to the conventional method, was verified for the same number of measurement points.