{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,12]],"date-time":"2025-10-12T02:03:28Z","timestamp":1760234608460,"version":"build-2065373602"},"reference-count":21,"publisher":"MDPI AG","issue":"2","license":[{"start":{"date-parts":[[2021,5,27]],"date-time":"2021-05-27T00:00:00Z","timestamp":1622073600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["MAKE"],"abstract":"<jats:p>In a gravity-free or microgravity environment, liquid metals without crystalline nuclei achieve a deep undercooling state. The resulting melts exhibit unique properties, and the research of this phenomenon is critical for exploring new metastable materials. Owing to the rapid crystallization rates of deeply undercooled liquid metal droplets, as well as cost concerns, experimental systems meant for the study of liquid metal specimens usually use low-resolution, high-framerate, high-speed cameras, which result in low-resolution photographs. To facilitate subsequent studies by material scientists, it is necessary to use super-resolution techniques to increase the resolution of these photographs. However, existing super-resolution algorithms cannot quickly and accurately restore the details contained in images of deeply undercooled liquid metal specimens. To address this problem, we propose the single-core multiscale residual network (SCMSRN) algorithm for photographic images of liquid metal specimens. In this model, multiple cascaded filters are used to obtain feature information, and the multiscale features are then fused by a residual network. Compared to existing state-of-the-art artificial neural network super-resolution algorithms, such as SRCNN, VDSR and MSRN, our model was able to achieve higher PSNR and SSIM scores and reduce network size and training time.<\/jats:p>","DOI":"10.3390\/make3020023","type":"journal-article","created":{"date-parts":[[2021,5,27]],"date-time":"2021-05-27T23:16:46Z","timestamp":1622157406000},"page":"453-466","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Single-Core Multiscale Residual Network for the Super Resolution of Liquid Metal Specimen Images"],"prefix":"10.3390","volume":"3","author":[{"given":"Keqing","family":"Ning","sequence":"first","affiliation":[{"name":"Institute of Photoelectronics Technology, School of Science Beijing Jiaotong University, Beijing 102603, China"},{"name":"School of Computer Science and Technology, North China University of Technology, Beijing 100144, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2049-1727","authenticated-orcid":false,"given":"Zhihao","family":"Zhang","sequence":"additional","affiliation":[{"name":"School of Computer Science and Technology, North China University of Technology, Beijing 100144, China"}]},{"given":"Kai","family":"Han","sequence":"additional","affiliation":[{"name":"School of Computer Science and Technology, North China University of Technology, Beijing 100144, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6151-318X","authenticated-orcid":false,"given":"Siyu","family":"Han","sequence":"additional","affiliation":[{"name":"School of Computer Science and Technology, North China University of Technology, Beijing 100144, China"}]},{"given":"Xiqing","family":"Zhang","sequence":"additional","affiliation":[{"name":"Institute of Photoelectronics Technology, School of Science Beijing Jiaotong University, Beijing 102603, China"}]}],"member":"1968","published-online":{"date-parts":[[2021,5,27]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"321","DOI":"10.1007\/s12217-018-9597-6","article-title":"Droplet Image Super Resolution Based on Sparse Representation and Kernel Regression","volume":"30","author":"Zou","year":"2018","journal-title":"Microgravity Sci. 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