A new perspective on terahertz image reconstruction based on linear spectral unmixing

Because of their unique capabilities, terahertz waves have demonstrated great potential in the concealed objects detection. However, the limitations in terahertz wave generation/detection devices and the disturbance from the pervasive water molecule absorption have hindered its wide deployment. Terahertz image reconstruction refers to the technique that extracts useful information from raw data to produce a meaningful image. In this paper, we present a new perspective of this technique. By defining the term differential spectrum as the difference between the transmitted spectrum and the received spectrum of the terahertz wave, we model the overall absorption effect as a linear combination of the differential spectrum of individual substance. Hence, a linear unmixing algorithm can be used to separate the overlapped substances represented by their weights, producing several image planes associated with each of the substances in the image scene. Experimental results have shown that the proposed algorithm can generate cleaner and sharper images compared to existing approaches. In addition, the object recognition process is inherent in the unmixing procedure thus making object detection and recognition easier.