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Journal Article
J-Net: Improved U-Net for Terahertz Image Super-Resolution
- Authors
- Woon-Ha Yeo, Seung-Hwan Jung, Seung Jae Oh, Inhee Maeng, Eui Su Lee, Han-Cheol Ryu
- Issue Date
- 2024-02
- Citation
- Sensors, v.24, no.3, pp.1-13
- ISSN
- 1424-8220
- Publisher
- Multidisciplinary Digital Publishing Institute (MDPI)
- Language
- English
- Type
- Journal Article
- Abstract
- 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.
- KSP Keywords
- Electromagnetic waves(EM), Frequency Range, High-resolution, Image feature, Image super resolution, Network Architecture, Peak-Signal-to-Noise-Ratio(PSNR), Quantitative comparison, Signal noise ratio(SNR), THz frequency, THz image
This work is distributed under the term of Creative Commons License (CCL)
(CC BY)
(CC BY)
