基于TGV正则化的MP-PET图像恢复-豆柴文库

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基于TGV正则化的MP-PET图像恢复
Introduction
Positronemissiontomography(PET)isanimportantmedicalimagingtechniquethatisusedtodetectanddiagnosevariousdiseasesandconditionsinthehumanbody.However,PETimagesareoftenplaguedbyanumberofartifactsandnoise,whichcanlimittheaccuracyandreliabilityoftheimagingprocess.Toovercomethesechallenges,variousimagerecoverytechniqueshavebeendeveloped,includingTGVregularizationandMPalgorithms.ThispaperexplorestheuseofTGVregularizationincombinationwithMPalgorithmsforthepurposeofimagerecoveryinPETimaging.
TGVregularization
Totalgeneralizedvariation(TGV)representsageneralizationoftotalvariationregularization,whichisapopularmethodusedinimageprocessingfordenoisingandotherpurposes.TGVregularizationisapowerfulmethodforsolvinginverseproblemsinimaging,andhasbeenusedinavarietyofapplications,includingMRI,CT,andPET.Essentially,TGVregularizationworksbypromotingthesparsityofthegradientofanimage,whichcanhelptoreducenoiseandartifactsandpreservefinedetailsintheimage.
MPalgorithms
Matchingpursuit(MP)algorithmsareanothersetoftechniquesthatarecommonlyusedinimageprocessingandreconstruction.MPalgorithmsaredesignedtoiterativelydecomposeanimageintoasumoffunctions,eachofwhichrepresentsadifferentaspectorcomponentoftheimage.Thegoalofthealgorithmistofindtheoptimalcombinationoffunctionsthatbestapproximatetheoriginalimage,whileminimizingsomepredefinedmeasureoferrorordistortion.
TGV-MP-PETimagerecovery
TheuseofTGVregularizationandMPalgorithmsincombinationisaneffectiveapproachtoimagerecoveryinPETimaging.Typically,theimagerecoveryprocessinvolvesseveralkeysteps,includingdenoising,reconstruction,andpost-processing.TGVregularizationisusedinthedenoisingsteptohelpreducetheamountofnoiseandartifactsinthePETimage,whilepreservingimportantdetailsandfeatures.TheMPalgorithmisthenusedtodecomposethedenoisedimageintoitsindividualcomponents,whichareanalyzedandreconstructedtoobtainahigh-qualityimage.
Experimentalresults
ToevaluatetheeffectivenessoftheTGV-MP-PETapproachtoimagerecovery,aseriesofexperiments