基于物理解耦与自适应特征提取的无监督图像去雾

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引用格式：，.基于物理解耦与自适应特征提取的无监督图像去雾[J].现代电子技术，2025，48（17）：77-84.

中图分类号：TN911.73-34；TP391.41 文献标识码：A 文章编号：1004-373X（2025）17-0077-08

Unsupervised image dehazing based on physical decoupling and adaptivefeatureextraction

YAN Zaishuang1，²，HE Peng1，² （1.HubeiKeyLaboratoryofIntellgentVisionBasedMonitoringforHydroelectricEngineering，Yichang443oChina; 2.CollegeofComputerandInformationTechnology，ChinaThreeGorgesUniversityYichang443Oo2，China）

Abstract：Unsupervisedlearning isadoptedtoimprovegeneralization inrealscenarios inmanyimage dehazingalgorithms. However，theexistingunsuperviseddehazingmethodsmostlyrelyontheCycleGAN（cycle-consistent generativeadversarial network）framework andrealizedehazing bysimplecyclesofdehazing andre-hazing，soitlacks efectiveconstraintsonthe generatedimagesandresultsinsuboptimalperformance.Moreover，thesemethodsfailtosuficientlyconsiderthephysical propertiesof haze inthe featureextraction，which causesasevere lossof texturedetails.Therefore，anunsupervised image dehazingnetworkbasedonphysicaldecouplingandadaptivefeatureextractionisproposed，andthenetworkmodelisnamed UPDA-Net.Specificalyaphysicaldecoupling network（PDN）isdesigned toseparateandestimatetheatmosphericlightand transmissionmapinhazyimagesbyestablishing physicalconstraintsbasedontheatmosphericscattering model.Thisdesign allowsformoreaccuraterestorationofimageilumination，andenhancesthephysicalconsistencyandclarityofthedehazing proces.Furthermore，anadaptive featureextraction module （AFEM）isdeveloped tointegrate thephysicalcharacteristicsof atmosphericlightand transmissionmaps.Byapplying theatmosphericscateringmodelinthefeaturespace，thismodule approximatesrelevantcharacteristicsandfuses thepotential physicalfeaturesofclearimages，soastoenhance themodel's featurerepresentationcapabilityandimprovethedetailandtexturequalityoftherestoredimages.Experimentalresults demonstratethattheproposedmethodoutperformssixmainstreamdehazingalgorithmsintermsofobjectiveevaluationandvisual quality on several public real image dehazing datasets.

Keywords：image dehazing;atmosphericscatering model;physical decoupling;parameter estimation；adaptive feature extraction;unsupervised learning;CycleGAN;imagerestoration

0 引言

图像去雾是计算机视觉领域的一个重要研究课题，近年来受到广泛关注。（剩余11993字）