Article(id=1263557428621263759, tenantId=1146029695717560320, journalId=1240618002186551303, issueId=1263557421033767765, articleNumber=null, orderNo=null, doi=10.3969/j.issn.1000-4653.2026.01.018, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=null, receivedDate=1743177600000, receivedDateStr=2025-03-29, revisedDate=null, revisedDateStr=null, acceptedDate=null, acceptedDateStr=null, onlineDate=1779185166745, onlineDateStr=2026-05-19, pubDate=1771948800000, pubDateStr=2026-02-25, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1779185166745, onlineIssueDateStr=2026-05-19, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1779185166745, creator=13701087609, updateTime=1779185166745, updator=13701087609, issue=Issue{id=1263557421033767765, tenantId=1146029695717560320, journalId=1240618002186551303, year='2026', volume='49', issue='1', pageStart='1', pageEnd='197', issueExtLink='null', onlineDate='null', pubDate='null', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=-1, specialIssue=null, createTime=1779185164936, creator=13701087609, updateTime=1779185226134, updator=13701087609, preIssue=null, nextIssue=null, ext={EN=IssueExt(id=1263557677813252309, tenantId=1146029695717560320, journalId=1240618002186551303, issueId=1263557421033767765, language=EN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=), CN=IssueExt(id=1263557677813252310, tenantId=1146029695717560320, journalId=1240618002186551303, issueId=1263557421033767765, language=CN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=)}, issueFiles=null}, startPage=177, endPage=188, ext={EN=ArticleExt(id=1263557429967635359, articleId=1263557428621263759, tenantId=1146029695717560320, journalId=1240618002186551303, language=EN, title=Contrast enhancement based dehazing algorithm for offshore tugboat sailing scene images, columnId=1240655021340160960, journalTitle=Navigation of China, columnName=Intelligent Shipping, runingTitle=null, highlight=null, articleAbstract=

This paper presents a contrast-enhancement-based dehazing algorithm to address detail loss, dim brightness, and color distortion in dehazed images of offshore tugboat sailing scenes with large sea-sky regions. First, atmospheric light estimation is optimized using quadtree segmentation to locate the light source in regions with minimal local pixel variance. Then, mean squared error contrast preserves image details, and a contrast overall cost function combined with an information loss function is used to find optimal transmission, enhancing contrast and making the sky region clearer. A fast guided filter further refines the transmission map, reducing block artifacts and maintaining real-time performance while restoring image authenticity. Finally, adaptive histogram equalization preserves contrast information in the sky, avoiding over-bright or over-dark areas. Experiments show that the image obtained using the proposed algorithm improves structural similarity, peak signal-to-noise ratio, and mean squared error by 15. 94%, 11. 46%, and 25. 82%, respectively, compared with the OCE method, while preventing color cast and halo effects, enhancing sea-sky boundary clarity, and meeting real-time requirements for restoring a realistic maritime environment.

, correspAuthors=Hongkun HE, authorNote=null, correspAuthorsNote=null, copyrightStatement=null, copyrightOwner=null, extLink=null, articleAbsUrl=null, sourceXml=null, magXml=null, pdfUrl=null, pdf=null, pdfFileSize=null, pdfExtLink=null, richHtmlUrl=null, mobilePdfUrl=null, reviewReport=null, pdfFirstPage=null, abstractGraph=null, abstractGraphContent=null, abstractVideo=null, citation=null, cebUrl=null, magXmlContent=null, mapNumber=null, authorCompany=null, fund=null, authors=null, authorsList=Hongkun HE, Zhengshu SHEN, Dazhi HUANG, Xi LIANG, Tingyu PENG, Yi XIAO), CN=ArticleExt(id=1263557432874288069, articleId=1263557428621263759, tenantId=1146029695717560320, journalId=1240618002186551303, language=CN, title=基于对比度增强的海上拖轮航行场景图像去雾算法, columnId=1240655021528904650, journalTitle=中国航海, columnName=智能航运, runingTitle=null, highlight=null, articleAbstract=

针对大面积海天区域的海上拖轮航行场景图像在去雾后存在的细节丢失、亮度偏暗和色彩失真等问题，提出了一种基于对比度增强的去雾算法。首先利用四叉树分割法对大气光取值进行优化，通过寻找局部像素方差最小的区域，以精确定位大气光源。接着利用均方误差对比度保留图像的细节，结合构建的对比度成本函数和信息损失函数，设计整体成本函数以找到最佳透射率，增强对比度，使天空区域更加细腻、鲜明。再利用快速引导滤波对透射率进行细化，消除块状伪影，保证图像的真实性。最后采用自适应直方图均衡算法，保留更多天空区域对比度信息，有效防止海天相依图像中出现天空或海面过曝或偏暗的现象。试验表明，与对比度增强算法（OCE）相比，应用所提算法去雾后的图像在结构相似度、峰值信噪比、均方误差等指标上分别平均提升了15.94%、11.46%、25.82%，有效避免了天空区域出现色偏和光晕现象，并解决了海天交界处边界不明显的问题，去雾速度也达到了实时性要求，能够还原真实的海上拖轮航行环境。

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何红坤（1988—），男，博士，研究方向为无人艇运动控制、视觉伺服、计算机视觉。

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何红坤（1988—），男，博士，研究方向为无人艇运动控制、视觉伺服、计算机视觉。

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