<?xml version="1.1" encoding="utf-8"?>
<article xsi:noNamespaceSchemaLocation="http://jats.nlm.nih.gov/publishing/1.1/xsd/JATS-journalpublishing1-mathml3.xsd" dtd-version="1.1" xmlns:xlink="http://www.w3.org/1999/xlink" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance"><front><journal-meta><journal-id journal-id-type="publisher-id">TACS</journal-id><journal-title-group><journal-title>Technology and Application of Computer Science</journal-title></journal-title-group><issn>2998-8926</issn><eissn>2998-8934</eissn><publisher><publisher-name>Art and Design</publisher-name></publisher></journal-meta><article-meta><article-id pub-id-type="doi">10.61369/TACS.2025050020</article-id><article-categories><subj-group subj-group-type="heading"><subject>Article</subject></subj-group></article-categories><title>基于人工智能的中医药古籍资源修复平台</title><url>https://artdesignp.com/journal/TACS/2/5/10.61369/TACS.2025050020</url><author>徐任,李明,谭蜜月,黄欣怡</author><pub-date pub-type="publication-year"><year>2025</year></pub-date><volume>2</volume><issue>5</issue><history><date date-type="pub"><published-time>2025-03-14</published-time></date></history><abstract>随着近年来国家战略对于中医药文化发展的要求，以《关于推进新时代古籍工作的意见》提出&amp;ldquo;推动中华优秀传统文化创造性转化、创新性发展&amp;rdquo;方针作为新时代古籍工作的指导思想。本文以开发一个基于人工智能的中医药古籍修复平台视角出发研究。此举既顺应了当今人工智能技术迅猛发展的趋势，又创新性的推动原来单一的古籍修复向着多维度、多层次的文化发展。同时，通过平台不仅让人们熟知古籍，更让古籍变得深入生活发挥价值。</abstract><keywords>人工智能,中医药古籍修复,图像超分辨率算法</keywords></article-meta></front><body/><back><ref-list><ref id="B1" content-type="article"><label>1</label><element-citation publication-type="journal"><p>&amp;nbsp;[1] 潘悦.古籍活起来，文脉传下去[J].语数外学习(初中版),2024(03):4.&amp;nbsp;[2] 覃燕梅.我国高校图书馆古籍文献保护工作研究[J].图书馆论坛,2007(04):36-38+115.&amp;nbsp;[3] 钟梦圆, 姜麟. 超分辨率图像重建算法综述[J]. 计算机科学与探索, 2022, 16(5): 972-990.&amp;nbsp;[4] 卢永美,卜令梅,陈黎,等. 基于深度学习的中医古文献临床经验抽取[J]. 四川大学学报（自然科学版）,2022, 59(2): 103-110.&amp;nbsp;[5] 胡中泽.基于对抗生成网络的古籍文献图像修复技术应用研究[D].中央民族大学,2018.DOI:CNKI:CDMD:2.1018.321146.&amp;nbsp;[6] 盛威,卢彦杰,刘伟,等.基于深度学习的中医古籍缺失文本修复研究[J].中华医学图书情报杂志,2022,31(08):1-7.&amp;nbsp;[7]Hassan M ,Illanko K ,Fernando N X .Single Image Super Resolution Using Deep Residual Learning[J].AI,2024,5(1):426-445.&amp;nbsp;[8]K. Chauhan et al., "Deep Learning-Based Single-Image Super-Resolution: A Comprehensive Review," in IEEE Access, vol. 11, pp. 21811-21830, 2023, doi: 10.1109/ACCESS.2023.3251396.&amp;nbsp;[9]NTIROGIANNIS K,GATOS B,PRATIKAKIS I.A combined approach for the binarization of handwritten document images[J].Pattern Recognition Letters,2014,35:3-15.&amp;nbsp;[10]XIAOYU L,BO Z,JING L,et al.Document rectification and illumination correction using a patch-based CNN[J].ACM Transactions on Graphics,2019,38(6):1-11.</p><pub-id pub-id-type="doi"/></element-citation></ref></ref-list></back></article>
