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<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 Technology</publisher-name></publisher></journal-meta><article-meta><article-id pub-id-type="doi">10.61369/TACS.2026060039</article-id><article-categories><subj-group subj-group-type="heading"><subject>Article</subject></subj-group></article-categories><title>高动态范围下多曝光图像清晰化处理方法研究</title><url>https://artdesignp.com/journal/TACS/3/6/10.61369/TACS.2026060039</url><author>张梦涵,靳豪,宋敬伟,李仕华</author><pub-date pub-type="publication-year"><year>2026</year></pub-date><volume>3</volume><issue>6</issue><history><date date-type="pub"><published-time>2026-03-28</published-time></date></history><abstract>针对传统基于 RGB 色彩空间下的多曝光融合算法，普遍存在效率低、色彩偏移等问题，针对这一现状，本文构建了一种 HSV 色彩空间下多曝光融合的方案。该方案在 V 通道上结合基于拉普拉斯金字塔的多曝光融合，并通过实验对融合算法进行了客观评价。与传统 RGB 色彩空间中基于拉普拉斯金字塔的多曝光融合相比，该方法获得的色彩更加丰富，融合效率相较传统方法也有所提高。</abstract><keywords>高动态范围, 色彩空间, 多曝光融合</keywords></article-meta></front><body/><back><ref-list><ref id="B1" content-type="article"><label>1</label><element-citation publication-type="journal"><p>[1]Xu K, Wang Q, Xiao H, et al. Multi-exposure image fusion algorithm based on improved weight function[J]. Frontiers in Neurorobotics, 2022, 16: 846580.
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