<?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.2025050047</article-id><article-categories><subj-group subj-group-type="heading"><subject>Article</subject></subj-group></article-categories><title>一种历史影像资料数字化智能修复及管理方案</title><url>https://artdesignp.com/journal/TACS/2/5/10.61369/TACS.2025050047</url><author>刘胜娟,李金龙</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>随着人工智能（AI）在图像处理领域的突破，对承载着珍贵历史文化信息的历史影像进行抢救性保护和再利用已成为可能。从物理载体（主要为胶片与磁带）的数字化转换，到基于AI与人工协同的精细画面修复，再到专业的媒体资产管理系统，系统性地研究并提出了一套针对历史影像视频资料（主要为胶片与磁带）的智能修复与管理解决方案。</abstract><keywords>历史影像,数字化,智能修复,媒资管理</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]National Film Preservation Foundation. The Film Preservation Guide[M/OL]. 4th ed. San Francisco: NFPF, 2020[2024-07-20].&amp;nbsp;[2]UNESCO. Recommendation on the Preservation of Documentary Heritage[R/OL]. Paris: UNESCO, 2015[2024-07-20].&amp;nbsp;[3]Zhang K, Li Y, Zuo W, et al. Deep learning for video restoration: A comprehensive review[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2021, 43(12): 4324-4345. DOI: 10.1109/TPAMI.2021.3053165.&amp;nbsp;[4] 腾讯优图. 基于多模态学习的百年老片修复系统[R]. 中国计算机大会(CNCC)报告, 2023.&amp;nbsp;[5]Wang Z, Chen J, Hoi S C H. Deep learning for image super-resolution: A survey[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2021, 43(10): 33653387.&amp;nbsp;[6] 潘永杰.基于智能算法的视频修复及超高清重制技术应用研究[J].广播与电视技术,2023,Vol.50(6).&amp;nbsp;[7]BBC Research &amp;amp; Development. Phantom frame: AI-based film restoration[R/OL]. (2022)[2024-07-20].&amp;nbsp;[8] 王惠明,欧臻彦,王倩男等.8K超高清视频质量客观评测方法研究[J].现代电视术,2022,(10):116-121.&amp;nbsp;[9] 李厦,张乾,汪芮等.网络视听节目平台视频技术质量评测方法及结果分析[J].广播与电视技术,2024,51(02):27-30.&amp;nbsp;[10] 张云珠.蓝光+双归档存储模式在融媒体内容管理系统中的应用[J].现代电视技术,2023,(05):153-155.</p><pub-id pub-id-type="doi"/></element-citation></ref></ref-list></back></article>
