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1、ANALYSISOFSIGNALDENOISINGMETHODSBASEDONWAVELETTRANSFORM^HEMIP・M・,2ALIM.A.DepartmentofElectronics,MESCollege,Marampally,Ernakulam,KeralaDepartmentofComputerApplications,Govt・£ngineeringCollege,Thrissur,KeralaEmail:shemipm@gmail.com,alimal23@gmail.comAbstract:Therealworldsignalsdonotexistwithou
2、tnoise.WaveletTransformbaseddenoisingisapowerfulmethodforsuppressingnoiseinsignals・Inthispaper,signaldenoisingbasedonDouble-DensityDiscreteWaveletTransform(DDDWT)andDual-TreeDiscreteWaveletTiansform(DTDWT)methodsarcimplementedwithoptimumvaluesofthresholdpointandlevelofdccomposition.Basedonthe
3、intensityofnoiseinthereceivedsignal,optimumvaluesofthresholdpointandlevelofdecompositionaredetermined・TheresultsintermsofRootMeanSquareError(RMSE)andSignaltoNoiseRatio(SNR)arcthencomparedwiththecorrespondingvaluesofDiscreteWaveletTransform(DWT)baseddenoisingmethod・Thepopulartestsignal;piece-r
4、egularcontaminatedwithAdditiveWhiteGaussianNoise(AWGN)ischosenfortheimplementation.TheresultsofMATLABsimulationsshowthatfortheselectedthresholdpointandlevelofdecomposition,theDDDWTandDTDWTperlormbetterthantheDWTmethod.Keywords:SignalDenoising,DiscreteWaveletTransform,Double-DensityDiscreteWav
5、eletTransfonn,Dual-TreeDiscreteWaveletTransform,Rootmeansquareerror.LINTRODUCTIONWavelettransformisawell-knowntoolthatfindsapplicationsintheareasofdigitalsignalandimageprocessing.Itprovidesamulti-resolutionanalysisbyrepresentingafunctionsimultaneouslyintimeandfrequencydomainsusingasetofanalyz
6、ingfunctionscalledwavelets[1].Thisgoodtime-frequencylocalityofwavelettransformmakesitusefulforprocessingofnon-stationaryandtransientsignals[2].Thewavelettransform,particularlyDiscreteWaveletTransform(DWT)performswellinnoiseremovalapplications.Asmulti-resolutionanalysisisnotpossiblewithothertr
7、ansformslikeFourierTransformandShortTimeFourierTransform,theycannotbeappliedmuchinsignaldenoisingapplications.Wavelettransformsarcofdifferenttypes・Thecritically-sampledformofthewavelettransformprovidesthemostcompactrepresentation.Butitlackssh