  Terahertz (THz) waves are electromagnetic waves in the 0.1 to 10 THz
frequency range, and THz imaging is utilized in a range of applications,
including security inspections, biomedical fields, and the non-destructive
examination of materials. However, THz images have low resolution due to the
long wavelength of THz waves. Therefore, improving the resolution of THz images
is one of the current hot research topics. We propose a novel network
architecture called J-Net which is improved version of U-Net to solve the THz
image super-resolution. It employs the simple baseline blocks which can extract
low resolution (LR) image features and learn the mapping of LR images to
highresolution (HR) images efficiently. All training was conducted using the
DIV2K+Flickr2K dataset, and we employed the peak signal-to-noise ratio (PSNR)
for quantitative comparison. In our comparisons with other THz image
super-resolution methods, JNet achieved a PSNR of 32.52 dB, surpassing other
techniques by more than 1 dB. J-Net also demonstrates superior performance on
real THz images compared to other methods. Experiments show that the proposed
J-Net achieves better PSNR and visual improvement compared with other THz image
super-resolution methods.
