J-Net: Improved U-Net for Terahertz Image Super-Resolution

• Published in: Sensors (Basel, Switzerland) Vol. 24; no. 3; p. 932
• Main Authors: Yeo, Woon-Ha, Jung, Seung-Hwan, Oh, Seung Jae, Maeng, Inhee, Lee, Eui Su, Ryu, Han-Cheol
• Format: Journal Article
• Language: English
• Published: Switzerland MDPI AG 01.01.2024
• Subjects: Antennas; convolutional neural network (CNN); deep learning; Electric waves; Electromagnetic radiation; Electromagnetic waves; Electromagnetism; Fourier transforms; image super-resolution; Medical imaging equipment; Neural networks; Security management; terahertz images; South Korea; Germany; deep learning; convolutional neural network (CNN); terahertz images; image super-resolution
• ISSN: 1424-8220; 1424-8220
• DOI: 10.3390/s24030932

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 a low resolution due to the long wavelength of THz waves. Therefore, improving the resolution of THz images is a current hot research topic. We propose a novel network architecture called J-Net, which is an improved version of U-Net, to achieve THz image super-resolution. It employs simple baseline blocks which can extract low-resolution (LR) image features and learn the mapping of LR images to high-resolution (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, J-Net 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 a better PSNR and visual improvement compared with other THz image super-resolution methods.