Article(id=1263557426708661118, tenantId=1146029695717560320, journalId=1240618002186551303, issueId=1263557421033767765, articleNumber=null, orderNo=null, doi=10.3969/j.issn.1000-4653.2026.01.005, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=null, receivedDate=1740412800000, receivedDateStr=2025-02-25, revisedDate=null, revisedDateStr=null, acceptedDate=null, acceptedDateStr=null, onlineDate=1779185166289, onlineDateStr=2026-05-19, pubDate=1771948800000, pubDateStr=2026-02-25, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1779185166289, onlineIssueDateStr=2026-05-19, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1779185166289, creator=13701087609, updateTime=1779185166289, 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=46, endPage=55, ext={EN=ArticleExt(id=1263557427790791564, articleId=1263557426708661118, tenantId=1146029695717560320, journalId=1240618002186551303, language=EN, title=Ship image dehazing method based on improved cyclic adversarial generative network, columnId=1240655017900823355, journalTitle=Navigation of China, columnName=Marine Traffic Safety, runingTitle=null, highlight=null, articleAbstract=

Foggy weather significantly degrades ship visibility and image quality, posing serious risks to navigation safety. Enhancing the dehazing performance of ship navigation images is therefore of great importance. To address the insufficient fog removal and poor detail restoration of existing dehazing methods in maritime scenarios, this study proposes an end-to-end ship image dehazing method that integrates an improved CycleGAN with attention mechanisms. A Squeeze-and-Excitation （SE）channel-attention module is introduced to aggregate feature maps, compress spatial information, and strengthen the network's ability to learn global representations. Multi-scale channel fusion is achieved through skip connections, which not only reduces computational complexity but also enables the model to better capture fog characteristics under complex atmospheric conditions and to process ship targets of different sizes. Furthermore, a Channel Attention module is incorporated to enhance feature selection and improve the restoration of ship contours and fine structural details. Quantitative evaluations and real fog-navigation experiments confirm the robustness of the proposed method, demonstrating consistent improvements over existing dehazing approaches across all tested metrics and navigation scenarios.

, correspAuthors=Bing HAN, 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=Xinqiang CHEN, Yucheng SUO, Bing HAN, Dezhi HAN, Jiajun XU, Zichuang WANG), CN=ArticleExt(id=1263557433780257738, articleId=1263557426708661118, tenantId=1146029695717560320, journalId=1240618002186551303, language=CN, title=基于改进循环对抗神经网络的船舶雾航图像去雾算法, columnId=1240655018068595518, journalTitle=中国航海, columnName=海上交通安全, runingTitle=null, highlight=null, articleAbstract=

雾天条件会显著降低船舶航行的可见性，影响船舶图像观测质量并威胁航行安全，因此提升雾航图像的去雾效果具有重要意义。针对传统船舶图像去雾方法在海事场景下存在的去雾不足、细节恢复能力差等问题，本文基于真实船舶航行图像构建试验数据集，提出一种融合改进循环对抗网络（CycleGAN）与注意力机制的端到端船舶图像去雾方法。该方法引入通道依赖建模机制（SE），对特征图进行压缩与聚合，增强网络的全局特征学习能力；通过跳跃连接实现多尺度通道融合，在降低计算量的同时有效捕获复杂大气条件下的雾特性，提高对不同尺度船舶目标的表征能力。此外，进一步加入通道卷积增强模块（CA）模块以强化特征选择能力，增强网络对船舶轮廓与局部细节的恢复效果。通过多项定量指标与真实雾航场景测试验证所提方法的鲁棒性，结果表明该方法在各类航行场景中均取得优于对比方法的去雾与细节恢复性能。

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陈信强（1987—），男，副教授，博士，研究方向为智能船舶、交通大数据挖掘、自动化码头等。

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陈信强（1987—），男，副教授，博士，研究方向为智能船舶、交通大数据挖掘、自动化码头等。

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