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Video Operations in the Gradient Domain

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1VideoOperationsintheGradientDomain

Iddo Drori Tommer Leyvand Shachar Fleishman Daniel Cohen-Or Hezy Yeshurun

Abstract

Fusionofimagesequencesisafundamentaloperationinnumerousvideoapplicationsandusuallyconsistsofsegmentation,mattingandcompositing.Wepresentaunifiedframeworkforperformingtheseoperationsonvideointhegradientdomain.Ourapproachconsistsof3Dgraphcutcomputationfollowedbyreconstructionofanew3DvectorfieldbysolvingthePoissonequation.Weintroducenewmethodsforfusingvideosmoothlyandseparatingforegroundelementsfromavideobackgroundforcompositingbydefiningsmoothandsharptransitionconstraintsonagradientvideocompositingequation.Wedemonstratetheapplicabilityofsmoothvideotransitionsbyfusingpairsforvideomosaics,videofolding,andvideotexturesynthesis,anddemonstratetheapplicabilityofsharpvideotransitionsbyvideosegmentation,videotrimapextractionand3Dcompositingintoanewsequence.Ourresultsdemonstratethatourmethodmaintainscoherenceofthevideomatteandcomposite,andavoidstemporalartifacts.

IndexTerms

Videoprocessing,gradientdomain,objectsegmentation,graphcuts,splinefitting,motionestima-tion.

I.INTRODUCTIONCOMPOSITINGvideoandseparatingforegroundvideoelementsfromavideoback-

groundforcompositing,aswellasbackgroundvideocompletion,areoperationsrequired

intheproductionofmostmodernmotionpictures.Smoothlyfusingpartsofvideoisimportant

foranumberofapplications:videomosaics,videotexturesynthesis,andvideofoldingbyreusing2

partsofvideofootage.Separatingforegroundandbackgroundisrequiredforcreatingavideo

mattewhichisusedforcompositingelementsintoanewbackground.

Compositingistheprocessofseamlesslycombiningmultipleimageorvideoregions.Itis

commonlyusedincreatingmosaics,editing,andtexturesynthesis.Imageandvideomosaics

areusedinapplicationsinwhichtherearemultiplesources,eachwithalimitedfieldofview

orfromadifferentorigin.Thegoalistomergethesesourcestoformasinglemosaic.Thisis

performedbyfirstaligningthesourcesbywarpingandthensmoothlyblendingtheoverlapping

regions.Imageandvideoeditingoperationsincludetemporaltransitions,placingafigureover

abackground,andseamlesslycombiningdifferentimageorvideoregions.Texturesynthesis

commonlyconsistsofsearchingforsimilarpatchesfollowedbymergingthemtogether.These

applicationsinvolvecompositingasanessentialstepoftheprocess.

Naturalmattingistheinverseproblemofderivingalphavaluesaroundobjectboundariesby

whichbackgroundandforegroundpixelsareblended.Theoperationsofcompositingandmatting

arethereforelinkedbythecompositingequationwhichdefinesthecolorofapixelasaconvex

combinationofforegroundandbackgroundpixels.Theinputtovideomattingisthecomposite

videoandatrimapwhichconsistsofthreegrayscalevaluesdenotingbackground,foreground

andunknownvideoregions.Mattingisderivedfromthecompositingequationandrequires

backgroundvideocompletionandforegroundestimationaswellasatrimap.Backgroundvideo

completionistheprocessoffillinginmissingregionsinvideobasedontemporalandspatial

information.Segmentationofavideointolayers,compositing,mattingandcompletionareall

inherentlylinkedbytheirrequiredinput-outputandusage.Computingatrimapandbackground

videocompletion,inturn,requireroughsegmentationoftheforegroundandcompositingofthe

completedvideobackground.3

A.Overview

Ourapproachistoworkin3Dwithvideovolumesinthegradientdomain.Therefore,we

representthecompositingequationforvideovolumesanddifferentiate.Workinginthegradient

domainallowsperforminglocaloperationstogetaglobaleffect.Thedifferentiationtransforms

theequationintodifferentialcoordinatesandresultsinthegradientvideocompositingequation.

Next,wezeroouttermsintheequationaccordingtotheapplication,whetheritrequiresa

smoothtransitionbetweenvideosorsharptransitionsbetweenuniformvideoregions.Anew

three-dimensionalvectorfieldresultsfromremovingtermsfromthegradientvideocompositing

equation.Thisvectorfieldisreconstructedtogetthefinalresult.Frame-by-framegradient

operationsgiverisetostrongtemporalartifactswhicharenoticeableinvideo,andtherefore,we

extendthereconstructionequationstothreedimensions.

Wedevelopanduseseveraltoolsinthiswork.Thefirstis3Dminimumgraphcutsandthe

secondisreconstructionfromamodified3DvectorfieldbysolvingthePoissonequation.The

typesofcutsanditerationsdifferforthedifferentapplicationsofvideofusionandseparation.

Thenewvectorfieldsaredefinedwithrespecttothecut,andtheresultofsolvingthe3DPoisson

equationisanewvideoormatteaccordingtotheapplication.Inadditionweuse3Dsplines

tofitpositionsoftrackerdataand2Dsplinestomarkregionsaroundforegroundelementsin

afewkey-framesandinterpolatebetweenthem.Finally,motionestimationisusedforthecut

computationandintemporalbackgroundvideocompletion.

Graphpartitioningtechniquesarecommonlyusedforimageandobjectsegmentation,for

stitchingtogetheralignedimagestoformmosaics,andfortexturesynthesis.Thegraphis

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极小值点 英语

极小值点 英语

极小值点 英语

The concept of local minima is a fundamental topic in the field of

optimization and decision-making. In many real-world scenarios,

individuals or organizations are faced with the challenge of finding

the best solution among a set of alternatives, and the notion of local

minima plays a crucial role in this process.

A local minimum is a point in a function or a landscape where the

value of the function is lower than the values of its immediate

neighbors, but it may not be the absolute lowest point in the entire

domain. This means that while a local minimum represents a

relatively good solution, it may not be the global optimum or the

best possible solution.

One of the primary challenges in optimization is the ability to

distinguish between local and global minima. In many cases, the

search for the optimal solution can become trapped in a local

Exposure Fusion

Exposure Fusion

ExposureFusion

TomMertens1JanKautz2FrankVanReeth1

1HasseltUniversity—EDM

transationaleUniversiteitLimburg

Belgium2UniversityCollegeLondon

UK

Abstract

Weproposeatechniqueforfusingabracketedexposure

sequenceintoahighqualityimage,withoutconvertingto

HDRfirst.Skippingthephysically-basedHDRassembly

stepsimplifiestheacquisitionpipeline.Thisavoidscamera

responsecurvecalibrationandiscomputationallyefficient.

Italsoallowsforincludingflashimagesinthesequence.

Ourtechniqueblendsmultipleexposures,guidedbysimple

qualitymeasureslikesaturationandcontrast.Thisisdone

inamultiresolutionfashiontoaccountforthebrightness

variationinthesequence.Theresultingimagequalityis

comparabletoexistingtonemappingoperators.

1.Introduction

Digitalcamerashavealimiteddynamicrange,whichis

lowerthanoneencountersintherealworld.Inhighdy-

namicrangescenes,apicturewilloftenturnouttobeunder-

泊松表面重建

泊松表面重建

EurographicsSymposiumonGeometryProcessing(2006)KonradPolthier,AllaSheffer(Editors)

PoissonSurfaceReconstruction

MichaelKazhdan1,MatthewBolitho1andHuguesHoppe2

1JohnsHopkinsUniversity,BaltimoreMD,USA2MicrosoftResearch,RedmondWA,USA

Abstract

WeshowthatsurfacereconstructionfromorientedpointscanbecastasaspatialPoissonproblem.ThisPoisson

formulationconsidersallthepointsatonce,withoutresortingtoheuristicspatialpartitioningorblending,and

isthereforehighlyresilienttodatanoise.Unlikeradialbasisfunctionschemes,ourPoissonapproachallowsa

hierarchyoflocallysupportedbasisfunctions,andthereforethesolutionreducestoawellconditionedsparse

linearsystem.Wedescribeaspatiallyadaptivemultiscalealgorithmwhosetimeandspacecomplexitiesarepro-

portionaltothesizeofthereconstructedmodel.Experimentingwithpubliclyavailablescandata,wedemonstrate

一种基于梯度域引导滤波的图像去雾算法

一种基于梯度域引导滤波的图像去雾算法

Computer Science and Application 计算机科学与应用, 2023, 13(9), 1739-1747

Published Online September 2023 in Hans. https:///journal/csa https:///10.12677/csa.2023.139172

文章引用: 唐骏, 王洪涛, 李文, 王世允, 钱佳, 宋扬, 邱卫根. 一种基于梯度域引导滤波的图像去雾算法[J]. 计算机

科学与应用, 2023, 13(9): 1739-1747. DOI: 10.12677/csa.2023.139172

一种基于梯度域引导滤波的图像去雾算法

唐 骏，王洪涛，李 文，王世允，钱 佳，宋 扬，邱卫根

江苏北方湖光光电有限公司，江苏 无锡

收稿日期：2023年8月18日；录用日期：2023年9月15日；发布日期：2023年9月22日

摘 要 利用暗通道先验理论进行单幅图像去雾时，通常采用最小值滤波算法估计透射率，该算法直接导致了图

像边缘模糊、出现光晕等现象，或天空等含大面积雾霾区域时，出现颜色失真等问题。梯度域引导滤波

在保持像素边缘方面的独特性能，结合针对天空区域去雾的分段颜色补偿方法，准确的解决了上述问题。

改进算法与其他去雾算法进行了仿真比较，实验结果表明：改进算法可对图像景物和天空进行准确的去

雾，与此同时该算法在颜色细节增强和高动态分布方面具有较大优势。

关键词

图像去雾，暗通道先验，最小值滤波，梯度域引导滤波，分段颜色补偿

An Image Defogging Algorithm Based on

Gradient Domain Guided Filtering

Jun Tang, Hongtao Wang, Wen Li, Shiyun Wang, Jia Qian, Yang Song, Weigen Qiu