數(shù)字圖像處理圖像增強(qiáng)課件

第四章數(shù)字圖像處理2.圖像增強(qiáng)ImageEnhancement:TheimagewillbesignificantlyimprovedifoneormoreofthefunctionscalledEnhancementareapplied.圖像增強(qiáng)：突出相關(guān)的專題信息，提高圖像的視覺應(yīng)用，使分析者更容易識(shí)別圖像內(nèi)容，從圖像中提取更有用的定量化信息； 輻射增強(qiáng)、空間增強(qiáng)以及光譜增強(qiáng)（變換）遙感影像灰度的頻率分布輻射增強(qiáng)遙感影像灰度的頻率分布CommonSymmetricandSkewedDistributionsinRSDataCommonly,thedistributionofDNs(graylevels)canbeunimodalandmaybeGaussian,althoughskewingisusual.Multimodaldistributionsresultifascenecontainstwoormoredominantclasseswithdistinctlydifferent(oftennarrow)rangesofreflectance.Min-Max

ContrastStretch+1StandardDeviationContrastStretchLinearexpansionofDN'sintothefullscale(0-255)isacommonoption.對(duì)比度拉伸

Thestartingpointistopointoutthatradiancesmeasuredbythesensorsvaryinintensity.Thevaluesarerestatedas(DNs)thatconsistofequalincrementsoverarange.ThesimplestmanipulationoftheseDNsistoincreaseordecreasetherangeofDNspresentandassignthisnewrangethegraylevelsavailablewithintherangelimit.LinearContrastEnhancement:Minimum-MaximumContrastStretchwhere:-BVinistheoriginalinputbrightnessvalue-quantkistherangeofthebrightnessvaluesthatcanbedisplayedontheCRT(e.g.,255),minkistheminimumvalueintheimage,

maxkisthemaximumvalueintheimage,and

BVoutistheoutputbrightnessvalueContrastStretchof

Landsat

TMBand4DataOriginalMinimum-maximum+1standarddeviationAllotheroriginalbrightnessvaluesbetween5and104arelinearlydistributedbetween0and255.LinearContrastEnhancement:Minimum-MaximumContrastStretchOriginalLinearNon-linearContrastStretchingThesearemostlynonlinearfunctionsthataffecttheprecisedistributionofdensities(onfilm)orgraylevels(inmonitorimage)indifferentways,sothatsomeexperimentationmayberequiredtooptimizeresults.Commonlyusedspecialstretchesinclude:1)PiecewiseLinear,2)LinearwithSaturation3)Logarithmic,4)Exponential5)ProbabilityDistributionFunction,and…LogarithmicandInverseLogPiecewisePiecewiseLinearContrastStretchingContrastStretchingofPredawnThermalInfraredDataofthetheSavannahRiverOriginalMinimum-maximum+1standarddeviation

LinearwithSaturationHistogramEqualization直方圖均衡

evaluatestheindividualbrightnessvaluesinabandofimageryandassignsapproximatelyanequalnumberofpixelstoeachoftheuser-specifiedoutputgray-scaleclasses(e.g.,32,64,and256).

appliesthegreatestcontrastenhancementtothemostpopulatedrangeofbrightnessvaluesintheimage.

reducesthecontrastintheverylightordarkpartsoftheimageassociatedwiththetailsofanormallydistributedhistogram.Statisticsfora64x64HypotheticalImagewithBrightnessValuesfrom0to74096totalHistogramEqualization5TransformationFunction,

ki

foreachindividualbrightnessvalueForeachbrightnessvaluelevelBViinthequantkrangeof0to7oftheoriginalhistogram,anewcumulativefrequencyvaluekiiscalculated:wherethesummationcountsthefrequencyofpixelsintheimagewithbrightnessvaluesequaltoorlessthanBVi,andnisthetotalnumberofpixelsintheentirescene(4,096inthisexample).Thehistogramequalizationprocessiterativelycomparesthetransformationfunctionkiwiththeoriginalvaluesofli,todeterminewhichareclosestinvalue.Theclosestmatchisreassignedtotheappropriatebrightnessvalue.

Forexample,weseethatk0

=

0.19isclosesttoL1=0.14.Therefore,allpixelsinBV0(790ofthem)willbeassignedtoBV1.Similarly,the1023pixelsinBV1willbeassignedtoBV3,the850pixelsinBV2willbeassignedtoBV5,the656pixelsinBV3willbeassignedtoBV6,the329pixelsinBV4willalsobeassignedtoBV6,andall448brightnessvaluesinBV5–7willbeassignedtoBV7.Thenewimagewillnothaveanypixelswithbrightnessvaluesof0,2,or4.Thisisevidentwhenevaluatingthenewhistogram.Whenanalystsseesuchgapsinimagehistograms,itisusuallyagoodindicationthathistogramequalizationorsomeotheroperationhasbeenapplied.OriginalHistogramequalizationSpecificpercentagelinearcontraststretchdesignedtohighlightthethermalplumeSpatialFilteringtoEnhanceLow-andHigh-FrequencydetailandEdgesSpatialfrequency,thenumberofchangesinbrightnessvalueperunitdistanceforanyparticularpartofanimage.空間增強(qiáng)SpatialfrequencyinRSimagerymaybeenhancedorsubduedusingspatialconvolutionfiltering

(卷積)

basedprimarilyontheuseofconvolutionmasks.VariousConvolutionMaskKernelsThesizeoftheneighborhoodconvolutionmaskorkernel(n)isusually3x3,5x5,7x7,or9x9.

SpatialConvolutionFiltering:MedianFilterAmedianfilterhascertainadvantageswhencomparedwithweightedconvolutionfilters,including:1)itdoesnotshiftboundaries,and2)theminimaldegradationtoedgesallowsthemedianfiltertobeappliedrepeatedlywhichallowsfinedetailtobeerasedandlargeregionstotakeonthesamebrightnessvalue(oftencalledposterization).SpatialFrequencyFilteringSpatialConvolutionFiltering:MinimumorMaximumFiltersOperatingononepixelatatime,thesefiltersexaminethebrightnessvaluesofadjacentpixelsinauser-specifiedradius(e.g.,3x3pixels)andreplacethebrightnessvalueofthecurrentpixelwiththeminimumormaximumbrightnessvalueencountered,respectively.SpatialFrequencyFilteringWewillconstrainourdiscussionto3x3convolutionmaskswithninecoefficients,ci,definedatthefollowinglocations: c1c2c3Masktemplate= c4c5c6 c7c8c9SpatialConvolutionFiltering111111111Alinear

spatialfilterisafilterforwhichthebrightnessvalue(BVi,j,out)atlocationi,jintheoutputimageisafunctionofsomeweightedaverage(linearcombination)ofbrightnessvalueslocatedinaparticularspatialpatternaroundthei,jlocationintheinputimage.Thecoefficients,c1,inthemaskaremultipliedbythefollowingindividualbrightnessvalues(BVi)intheinputimage: c1xBV1c2xBV2c3xBV3Masktemplate= c4xBV4c5xBV5c6xBV6 c7xBV7c8xBV8c9xBV9TheprimaryinputpixelunderinvestigationatanyonetimeisBV5=BVi,jSpatialConvolutionFiltering111111111SpatialConvolutionFiltering:LowFrequencyFilterSpatialFrequencyFilteringSpatialConvolutionFiltering:HighFrequencyFilterHigh-passfilteringisappliedtoimagerytoremovetheslowlyvaryingcomponentsandenhancethehigh-frequencylocalvariations.Onehigh-frequencyfilter(HFF5,out)iscomputedbysubtractingtheoutputofthelow-frequencyfilter(LFF5,out)fromtwicethevalueoftheoriginalcentralpixelvalue,BV5:SpatialFrequencyFilteringSpatialConvolutionFiltering:Unequal-weightedsmoothingFilter0.250.500.250.5010.500.250.500.25111121111SpatialConvolutionFiltering:EdgeEnhancementFormanyRSEarthscienceapplications,themostvaluableinformationthatmaybederivedfromanimageiscontainedintheedgessurroundingvariousobjectsofinterest.Edgeenhancementdelineatestheseedgesandmakestheshapesanddetailscomprisingtheimagemoreconspicuousandperhapseasiertoanalyze.Edgesmaybeenhancedusingeitherlinearornonlinearedgeenhancementtechniques.SpatialConvolutionFiltering:DirectionalFirst-DifferenceLinearEdgeEnhancementTheresultofthesubtractioncanbeeithernegativeorpossible,thereforeaconstant,K(usually127)isaddedtomakeallvaluespositiveandcenteredbetween0and255SpatialConvolution

Filtering:

High-passFiltersthatAccentuateorSharpenEdges-1-1-1-19-1-1-1-11-21-25-21-21SpatialConvolutionFiltering:

LinearEdgeEnhancement-Embossing00010-1000EmbossEast001000-100EmbossNWSpatialFrequencyFiltering0-10-14-10-10-1-1-1-18-1-1-1-11-21-24-21-211111-71111SpatialConvolutionFiltering:EdgeEnhancementTheLaplacianisasecondderivativeandisinvarianttorotation,meaningthatitisinsensitivetothedirectioninwhichthediscontinuities(point,line,andedges)run.SpatialFrequencyFilteringPrincipalComponentsAnalysis

transformationoftherawremotesensordatausingPCAcanresultinnewprincipalcomponentimagesthatmaybemoreinterpretablethantheoriginaldata.

mayalsobeusedtocompresstheinformationcontentofanumberofbandsofimagery(e.g.,sevenTMbands)intojusttwoorthreetransformedprincipalcomponentimages.Theabilitytoreducethedimensionality(i.e.,thenumberofbands)fromntotwoorthreebandsisanimportanteconomicconsideration,especiallyifthepotentialinformationrecoverablefromthetransformeddataisjustasgoodastheoriginalremotesensordata.光譜增強(qiáng)與變換Thespatialrelationshipbetweenthefirsttwoprincipalcomponents:(a)Scatter-plotofdatapointscollectedfromtworemotelybandslabeledX1andX2withthemeansofthedistributionlabeledμ1andμ2.(b)AnewcoordinatesystemiscreatedbyshiftingtheaxestoanX

system.ThevaluesforthenewdatapointsarefoundbytherelationshipX1=X1–μ1andX2=