{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,24]],"date-time":"2026-10-24T01:49:17Z","timestamp":1792806557076,"version":"4.5.0"},"reference-count":26,"publisher":"MDPI AG","issue":"3","license":[{"start":{"date-parts":[[2024,1,31]],"date-time":"2024-01-31T00:00:00Z","timestamp":1706659200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Research Foundation (NRF)","award":["NRF-2021R1F1A1059493"],"award-info":[{"award-number":["NRF-2021R1F1A1059493"]}]},{"name":"National Research Foundation (NRF)","award":["23ZB1130"],"award-info":[{"award-number":["23ZB1130"]}]},{"name":"Electronics and Telecommunications Research Institute (ETRI) grant","award":["NRF-2021R1F1A1059493"],"award-info":[{"award-number":["NRF-2021R1F1A1059493"]}],"id":[{"id":"https:\/\/ror.org\/03ysstz10","id-type":"ROR","asserted-by":"crossref"}]},{"name":"Electronics and Telecommunications Research Institute (ETRI) grant","award":["23ZB1130"],"award-info":[{"award-number":["23ZB1130"]}],"id":[{"id":"https:\/\/ror.org\/03ysstz10","id-type":"ROR","asserted-by":"crossref"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>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.<\/jats:p>","DOI":"10.3390\/s24030932","type":"journal-article","created":{"date-parts":[[2024,1,31]],"date-time":"2024-01-31T10:44:24Z","timestamp":1706697864000},"page":"932","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":16,"title":["J-Net: Improved U-Net for Terahertz Image Super-Resolution"],"prefix":"10.3390","volume":"24","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-1479-7383","authenticated-orcid":false,"given":"Woon-Ha","family":"Yeo","sequence":"first","affiliation":[{"name":"Department of Artificial Intelligence Convergence, Sahmyook University, 815 Hwarang-ro, Nowon-gu, Seoul 01795, Republic of Korea"},{"name":"Taean AI Industry Promotion Agency (TAIIPA), Taean County 32154, Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Seung-Hwan","family":"Jung","sequence":"additional","affiliation":[{"name":"Department of Artificial Intelligence Convergence, Sahmyook University, 815 Hwarang-ro, Nowon-gu, Seoul 01795, Republic of Korea"},{"name":"Taean AI Industry Promotion Agency (TAIIPA), Taean County 32154, Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Seung Jae","family":"Oh","sequence":"additional","affiliation":[{"name":"YUHS-KRIBB Medical Convergence Research Institute, Yonsei University College of Medicine, 50-1 Yon-sei-ro, Seodaemun-gu, Seoul 03722, Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Inhee","family":"Maeng","sequence":"additional","affiliation":[{"name":"YUHS-KRIBB Medical Convergence Research Institute, Yonsei University College of Medicine, 50-1 Yon-sei-ro, Seodaemun-gu, Seoul 03722, Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1585-3412","authenticated-orcid":false,"given":"Eui Su","family":"Lee","sequence":"additional","affiliation":[{"name":"Electronics and Telecommunications Research Institute (ETRI), Daejeon 34129, Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2867-1127","authenticated-orcid":false,"given":"Han-Cheol","family":"Ryu","sequence":"additional","affiliation":[{"name":"Department of Artificial Intelligence Convergence, Sahmyook University, 815 Hwarang-ro, Nowon-gu, Seoul 01795, Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2024,1,31]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"22200","DOI":"10.1364\/OE.394943","article-title":"Adaptive terahertz image super-resolution with adjustable convolutional neural network","volume":"28","author":"Li","year":"2020","journal-title":"Opt. 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