A Complexity-Reduction Sub-Terahertz Image Sensing Method using Adaptive Signal Processing

Terahertz-wave sensing is expected to be used for high-resolution metal detection because of its broadband nature and object transparency. However, when terahertz-band near-field image sensing is conducted using array antennas, the amount of signal processing increases significantly with the resolution. We propose a low-complexity imaging algorithm that changes the sensing resolution adaptively according to the area where an object may exist without compromising the object detection capability. Experimental results using actual measured data show that the proposed method reduces the computational complexity to less than one-third of that of conventional linear interpolation methods without degrading the quality of the sensed image.