A novel visualization method of terahertz time-domain spectroscopy (THz-TDS) image is presented, which is based on
principal component analysis (PCA) technique. The proposed method include three processing steps: firstly, the THz-
TDS image is preprocessed using a spatial vector filtering technique to denoise. Secondly, the THz-TDS image is
transformed from spatio-temporal domain to spatio-spectral domain, and the transformed image can be viewed as a
multispectral image whose spectral dimensionality D is equal to the sampled number of THz-TDS pulse at each pixel.
Thirdly, each of spectrum vector at a pixel is viewed as a point in D dimensional space, the covariance matrix of pixels
can be computed, and then three eigenvectors corresponding to the first 3 largest eigenvalues are found by PCA
technique. the THz-TDS image is projected along these three eigenvectors. By normalizing these 3 principal component
images and mapping them into the RGB space, we can get a synthetic color image as a visualization result of the THz-
TDS image. Due to vector-based dimensionality reduction, the proposed method can provide more visual information of
the THz-TDS image than scalar-based visualization techniques. Finally, experimental results are provided to demonstrate
the performance of the proposed method.