简介:ConsiderabackwardheatequationinaboundeddomainΩR2withthenoisydataintheinitialtimegeometry.Theaimistofindthetemperaturefor0<ε<t<T.Forthisill-posedproblem,theauthorsgiveacontinuousdependenceestimateofthesolution.Moreover,theconvergencerateoftheapproximatesolutionisalsogiven.
简介:一个方法为转换被描述在Laguerre多项式使用扩大并且把Mellin变换变换成Laplace变换的Mellin变换,然后,Laplace变换被合适的替换变换成第一个客气的卷绕旋转积分方程。那么获得的不可分的方程是一个提出病的问题,我们使用花键规则化解决它。方法的性能被在文学可得到的测试函数的倒置说明[J。Inst。数学。与Appl。20(1977),p。73],[J。数学。Comp。53(1989),p。589],[J。Sci。Stat。Comp。4(1983),p。164]。方法的有效性被获得的结果显示出借助于桌子和图示威了。
简介:AnewmethodforapproximatingtheinerseLaplacetransformispresented.WefirstchangeourLaplacetransformequationintoaconvolutiontypeintegralequation,whereTikhonovregularizationtechniquesandtheFouriertransformationareeasilyapplied.WefinallyobtainaregularizedapproximationtotheinverseLaplacetransformasfinitesum
简介:Inthispaper,weconsidertheCauchyproblemfortheLaplaceequation,whichisseverelyill-posedinthesensethatthesolutiondoesnotdependcontinuouslyonthedata.AmodifiedTikhonovregularizationmethodisproposedtosolvethisproblem.Anerrorestimatefortheaprioriparameterchoicebetweentheexactsolutionanditsregularizedapproximationisobtained.Moreover,anaposterioriparameterchoiceruleisproposedandastableerrorestimateisalsoobtained.Numericalexamplesillustratethevalidityandeffectivenessofthismethod.
简介:这份报纸的目的是在在SPECT重建治好TV模型的staircasing人工制品调查infimal卷绕旋转规则化的能力。我们作为一个凸的三块的优化问题与infimal卷绕旋转规则化提出SPECT重建的问题并且以涉及它的客观函数的函数的最近操作符由定点方程的一个系统描绘它的解决方案。我们然后基于定点方程开发一个新奇定点最近算法。而且,我们介绍古典MLEM(最大可能性的的期望最大化)激发的一个preconditioning矩阵算法。我们证明建议算法的集中。数字结果被包括证明infimal卷绕旋转规则化能够有效地减少staircasing人工制品,当以signal-to-noise比率和系数恢复对比维持可比较的图象质量时。
简介:Regularizationmethodisaneffectivemethodforsolvingill-posedequation.Inthispapertheunbiasedestimationformulaofunitweightstandarddeviationintheregularizationsolutionisderivedandtheformulaisverifiedwithnumericalcaseof1000sampledatabyuseofthetypicalill-posedequation,i.e.theFredholmintegrationequationofthefirstkind.
简介:Tomakeuseofthepriorknowledgeoftheimagemoreeffectivelyandrestoremoredetailsoftheedgesandstructures,anovelsparsecodingobjectivefunctionisproposedbyapplyingtheprincipleofthenon-localsimilarityandmanifoldlearningonthebasisofsuper-resolutionalgorithmviasparserepresentation.Firstly,thenon-localsimilarityregularizationtermisconstructedbyusingthesimilarimagepatchestopreservetheedgeinformation.Then,themanifoldlearningregularizationtermisconstructedbyutilizingthelocallylinearembeddingapproachtoenhancethestructuralinformation.Theexperimentalresultsvalidatethattheproposedalgorithmhasasignificantimprovementcomparedwithseveralsuper-resolutionalgorithmsintermsofthesubjectivevisualeffectandobjectiveevaluationindices.
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简介:Imagerestorationisoftensolvedbyminimizinganenergyfunctionconsistingofadata-fidelitytermandaregularizationterm.Aregularizedconvextermcanusuallypreservetheimageedgeswellintherestoredimage.Inthispaper,weconsideraclassofconvexandedge-preservingregularizationfunctions,I.e.,multiplicativehalf-quadraticregularizations,andweusetheNewtonmethodtosolvethecorrespondinglyreducedsystemsofnonlinearequations.AteachNewtoniterate,thepreconditionedconjugategradientmethod,incorporatedwithaconstraintpreconditioner,isemployedtosolvethestructuredNewtonequationthathasasymmetricpositivedefinitecoefficientmatrix.Theigenvalueboundsofthepreconditionedmatrixaredeliberatelyderived,whichcanbeusedtoestimatetheconvergencespeedofthepreconditionedconjugategradientmethod.Weuseexperimentalresultstodemonstratethatthisnewapproachisefficient,andtheeffectofimagerestorationisr0easonablywell.
简介:Inthispaperweconsideranon-standardinverseheatconductionproblemfordeterminingsurfaceheatfluxfromaninteriorobservationwhichappearsinsomeappliedsubjects.Thisproblemisill-posedinthesensethatthesolution(ifitexists)doesnotdependcontinuouslyonthedata.AFouriermethodisappliedtoformulatearegularizedapproximationsolution,andsomesharperrorestimatesarealsogiven.