# Super-resolution of THz time-domain images based on low-rank representation

arXiv: https://arxiv.org/abs/2312.13820 · HTML: https://mlsanity.com/paper/2312.13820/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 4 of its 21 references that are in the map.
- First posted on arXiv 21 Dec 2023.

## Builds on (most cited first, 4 of 4)

- [Image Super-Resolution Using Deep Convolutional Networks](https://mlsanity.com/paper/1501.00092/) (2014)
- [A convex formulation for hyperspectral image superresolution via subspace-based regularization](https://mlsanity.com/paper/1411.4005/) (2014)
- [Computational Image Enhancement for Frequency Modulated Continuous Wave (FMCW) THz Image](https://mlsanity.com/paper/1802.05457/) (2018)
- [Beam-Shape Effects and Noise Removal from THz Time-Domain Images in Reflection Geometry in the 0.25-6 THz Range](https://mlsanity.com/paper/2203.00417/) (2022)

## Abstract (arXiv)

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.