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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 
Citation
 SENSORS, Vol.24(3) : 932, 2024-02 
Journal Title
SENSORS
Issue Date
2024-02
Keywords
convolutional neural network (CNN) ; deep learning ; image super-resolution ; terahertz images
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. © 2024 by the authors.
Files in This Item:
T202401565.pdf Download
DOI
10.3390/s24030932
Appears in Collections:
1. College of Medicine (의과대학) > Research Institute (부설연구소) > 1. Journal Papers
Yonsei Authors
Maeng, In hee(맹인희)
Oh, Seung Jae(오승재)
URI
https://ir.ymlib.yonsei.ac.kr/handle/22282913/198723
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