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Compressed Sensing based Terahertz imaging (CS-THz) is a computationalimaging technique. It uses only one THz receiver to accumulate the randommodulated image measurements where the original THz image is reconstruct fromthese measurements using compressed sensing solvers. The advantage of theCS-THz is its reduced acquisition time compared with the raster scan mode.However, when it applied to large-scale two-dimensional (2D) imaging, theincreased dimension resulted in both high computational complexity andexcessive memory usage. In this paper, we introduced a novel CS-based THzimaging system that progressively compressed the THz image column by column.Therefore, the CS-THz system could be simplified with a much smaller sizedmodulator and reduced dimension. In order to utilize the block structure andthe correlation of adjacent columns of the THz image, a complex-valued blocksparse Bayesian learning algorithm was proposed. We conducted systematicevaluation of state-of-the-art CS algorithms under the scan based CS-THzarchitecture. The compression ratios and the choices of the sensing matriceswere analyzed in detail using both synthetic and real-life THz images.Simulation results showed that both the scan based architecture and theproposed recovery algorithm were superior and efficient for large scale CS-THzapplications.

Scan-based Compressed Terahertz Imaging and Real-Time Reconstruction via the Complex-valued Fast Block Sparse Bayesian Learning Algorithm