Super-resolution of THz time-domain images based on low-rank representation
This work addresses resolution limitations in THz imaging, which is important for materials science and spectroscopy, but it appears incremental as it builds on existing super-resolution techniques for a specific domain.
The paper tackles the problem of low spatial resolution in terahertz time-domain images, which is limited by beam waist and acquisition step size, by presenting a super-resolution approach that restores images acquired with medium-to-big step sizes, resulting in higher resolution and removal of blur and noise across frequency bands from 0.5 to 3.5 THz.
Terahertz time-domain spectroscopy (THz-TDS) employs sub-picosecond pulses to probe dielectric properties of materials giving as a result a 3-dimensional hyperspectral data cube. The spatial resolution of THz images is primarily limited by two sources: a non-zero THz beam waist and the acquisition step size. Acquisition with a small step size allows for the visualisation of smaller details in images at the expense of acquisition time, but the frequency-dependent point-spread function remains the biggest bottleneck for THz imaging. This work presents a super-resolution approach to restore THz time-domain images acquired with medium-to-big step sizes. The results show the optimized and robust performance for different frequency bands (from 0.5 to 3.5 THz) obtaining higher resolution and additionally removing effects of blur at lower frequencies and noise at higher frequencies.