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基于透射率自适应约束修正的图像去雾算法.docx

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基于透射率自适应约束修正的图像去雾算法 Title:AnImageDehazingAlgorithmbasedonAdaptiveConstraintCorrectionusingTransmittanceEstimation Abstract: Imagedehazingisessentialinapplicationssuchasoutdoorimaging,remotesensing,andautonomousnavigation.However,hazyweatherconditionscansignificantlydegradeimagequalitybyreducingcontrast,colorfidelity,andoverallvisibility.Inthispaper,weproposeanovelimagedehazingalgorithmbasedonadaptiveconstraintcorrectionusingtransmittanceestimation.Byutilizingatransmissionmap,ourmethodaimstoestimateandenhancevisualcontentbyeffectivelyremovinghaze.Experimentalresultsdemonstratethattheproposedalgorithmoutperformsexistingapproachesintermsofimagequality,visibility,andcolorfidelity. 1.Introduction Hazeisachallengingprobleminimageprocessingduetoitsnegativeimpactonimagequality.Traditionaldehazingalgorithmsusuallyassumeaglobalatmosphericmodel,whichcanresultininaccurateresultsincomplexscenes.Toaddresstheselimitations,weproposeanimagedehazingalgorithmthatincorporatesadaptiveconstraintcorrectionusingtransmittanceestimation.Thisapproachimprovestheaccuracyofhazeremovalbyconsideringscene-specificpropertiesandadjustingcontrastandcolorpropertiesaccordingly. 2.Problemformulation 2.1Hazemodel Thedegradationmodelusedinthisalgorithmassumesasceneaffectedbyhaze,wheretheobservedimagecanberepresentedasacombinationofsceneradianceandatmosphericlightattenuatedbyhaze.ThemodelisdescribedbyEquation1. Equation1:I(x)=J(x)t(x)+A(1-t(x)) whereI(x)istheobservedimage,J(x)isthesceneradiance,t(x)isthetransmittance,Aistheatmosphericlight,andxdenotesapixellocation. 2.2Transmittanceestimation Transmittanceestimationplaysavitalroleinimagedehazingalgorithms.Inourapproach,weproposeanovelmethodbasedonadaptiveconstraintcorrection.Thismethodemploysatransmissionmaptoestimatethescenetransmittancemoreaccurately.Thetransmissionmapisobtainedbyapplyinglocalfilteringandstatisticalanalysistotheobservedhazyimage.Theestimatedtransmittanceisthenutilizedtoenhancethevisualcontentandremovehazeeffectively. 3.AdaptiveConstraintC