THz-Super-Resolution Generative Adversarial Network: Deep-Learning-Based Super-Resolution Imaging Using Terahertz Time-Domain Spectroscopy
Pengfei Zhu, Ziang Wei, Стефано Сфарра, Rubén Usamentiaga, Gunther Steenackers, Andreas Mandelis, Xavier Maldague, Hai Zhang
Abstract
Constrained by Abbe diffraction, terahertz time-domain spectroscopy utilizing photoconductive antennas (PCA) is limited to submillimeter spatial resolution and requires several hours to complete a scan. Existing solutions involve using high-precision probes or solid immersion lenses to enable super-resolution imaging. However, these techniques necessitate precise experimental setups and controlled environments, rendering them unsuitable for meeting requirements of field use in areas, such as nondestructive testing, biomedicine, and nanotechnology. Here, the deep learning-based technique THz-super-resolution generative adversarial network (THz-SRGAN) was introduced in the THz-SR imaging field for the first time. The new imaging method not only overcomes the Abbe diffraction limit at minimal cost but also extracts valuable information regarding the physical properties of imaged objects within a narrow spatial field. Furthermore, a Richardson–Lucy algorithm was developed for THz-SR imaging and compared the performance with that of THz-SRGAN. The experimental results demonstrate that the proposed THz-SRGAN method achieves the most significant improvement in spatial resolution to-date.