Topic-12-Color-Image-Processing 彩色圖像處理

ColorImageProcessingColorImageProcessingTechniquesinPseudo-ColorImageprocessingIntensitySlicingColorLUTDesignsNoiseinColorImagesNoiseinRGBNoiseinHSVSmoothingofColorImagesSharpeningofColorImagesOutlineEECE-5626:ColorImageProcessing1/24Pseudo-ColorImageProcessingAblack-and-whiteimageistransformedintoa"color"imageusingpixel-pointprocessing(colorLUTs).Themainapplicationisforhumanvisualizationandinterpretationofgray-leveldetails.Theprincipalchallengeistoselectapaththroughthecolorspacethatcan“amplify”thelowcontrastdetailsintheimage.Techniques:IntensitySlicingColorLUTdesign(Gray-leveltoColorTransformations)EECE-5626:ColorImageProcessing2/24Ifanimageisviewedasa2-D

intensityfunction,thenthis

intensitycanbe"sliced"bya

planeparalleltothecoordinate

plane.Ifweassigndifferentcolors

oneachsideoftheplane,any

pixelwithgraylevelabovethe

planeiscodedwithonecolor

andanypixelbelowtheplane

iscodedwithanothercolor.Theresultisatwocolorimageasshown.Thisapproachcanbeextendedtomorethantwoplanes.IntensitySlicingEECE-5626:ColorImageProcessing3/24IntensitySlicing(2)Example

Two-levelslicing:

Inthisexample,thegray-level255isassignedacoloryellowhiletherestofthegray-levels,[0–254],areassignedcolorblue.Thegray-level255signifiesfailureinaweld.EECE-5626:ColorImageProcessing4/24IntensitySlicing(3)Example:Eight-levelslicingEECE-5626:ColorImageProcessing5/24ColorLUTDesignsTheseLook-UpTablesdefineamappingfromthe1-Dgray-levelspacetothe3-DRGBcolorspace.ThismappingisgivenbythreePVMs,oneforeachprimarycolor.Almostallmodernimageprocessingboardscontain(programmable)hardwaretablesbetweenaframebufferandamonitor.EECE-5626:ColorImageProcessing6/24ColorLUTDesigns(2)Thesegray-leveltransformationsessentiallyareuniquepathsfromtheblackintensity(0)tothewhiteintensity(1)insuchawaythattheassignedcolorscanaidinvisualizingandidentifyingimagefeatures.EECE-5626:ColorImageProcessing7/24ColorLUTDesigns(3)Designofthesetransformationsrequiresagoodknowledgeofthecolortheory.Specifically,ifweassigncomplementarycolorstotheadjacentgray-levelsthenwecanvisualizethesegray-levelsbetter.EECE-5626:ColorImageProcessing8/24ColorLUTDesigns(4)Sometypicaltransformationsare:SpectrumorRainbowEECE-5626:ColorImageProcessing9/24ColorLUTDesigns(5)TypicalTransformations(continued)SpectrumorRainbow:ExampleEECE-5626:ColorImageProcessing10/24ColorLUTDesigns(6)TypicalTransformations(continued)SoftColors:Hereeachtransformationissinefunctionofthesamefrequencybutdifferentphase.Thisproducessoftcolorsandisusefulinenhancing"busy"details.EECE-5626:ColorImageProcessing11/24ColorLUTDesigns(7)TypicalTransformations(continued)SoftColors:ExampleEECE-5626:ColorImageProcessing12/24ColorLUTDesigns(8)TypicalTransformations(continued)SoftColors:ExampleEECE-5626:ColorImageProcessing13/24ColorLUTDesigns(9)TypicalTransformations(continued)Bitcolor:Hereeachbitplaneisassignedadifferentcolor.

Forexample,ifwerepresentapixelinbinaryas b7b6b5b4b3b2b1b0 thenonepossibleschemeis: RRR

GGG

BBEECE-5626:ColorImageProcessing14/24ColorLUTDesigns(10)TypicalTransformations(continued)Random:Herecolorschemesareassignedinarandomfashion.Usefulforimagesthathaveverysmoothappearance.EECE-5626:ColorImageProcessing15/24ColorLUTDesigns(11)PredefinedColormaps:EECE-5626:ColorImageProcessing16/24NoiseinColorImagesColorimagesareacquired(orformed)viacolorcamerasthatpredominantlyusetheRGBcolormodelandemployCCDarraysensorsforeachR,G,andBcolor.Henceitisreasonabletomodela“noisy”colorimageasbeingformedbythecorresponding“noisy”R,G,andBcomponentimages.Thenoisyimagemodelthenis where

η

isanequivalentRGBcolornoiseimagewhileeachnoisevariable,

ηR,

ηG,or

ηB

isanindependentnoisefield.EECE-5626:ColorImageProcessing17/24NoiseinColorImages(2)Example:NoiseinRGBspaceversusHSVspacef=imread([imagedir,'Fig0604(a)(iris).tif']);%Loadimage

figure;imshow(f);%Showimageg=imnoise(f,'gaussian',0,0.1);%AddNoiseinRGBspacefigure;imshow(g);%Showimagew=rgb2hsv(f);%RGBtoHSVConversionw=imnoise(w,'gaussian',0,0.1);%AddNoiseinHSVspacew=hsv2rgb(w);%HSVtoRGBconversionfigure;imshow(w);%ShowimageOriginalImageNoiseinRGBNoiseinHSVEECE-5626:ColorImageProcessing18/24ColorImageSmoothingSpatialaveragingorsmoothingofmonochromeimagesisaccomplishedbyconvolvingwithamaskwhichissymmetric(toavoidphasedistortionproblems).Theprocessofsmoothingacolorimageisformulatedinasimilarfashion,exceptthatinsteadofsinglepixelswenowhavethreepixelsateachspatiallocationasshownbelow:EECE-5626:ColorImageProcessing19/24ColorImageSmoothing(2)LetS[m,n]denoteaneighborhoodcenteredat(m,n)inacolorimage.ThentheaverageofanRGBimageisgivenby whereKisthenumberofpixelsintheneighborhood.Thustheaveragingoveraneighborhoodcanbecarriedouteitheronthecolorvectorspaceorontheindividualcomponentbasis.EECE-5626:ColorImageProcessing20/24ColorImageSmoothing(3)Smoothingsteps:Extractthethreecomponentimages:

>>fR=f(:,:,1);fG=f(:,:,2);fB=f(:,:,3);

Filtereachcomponentsindividually

>>fR_filt=imfilter(fR,h);

>>fG_filt=imfilter(fG,h);

>>fB_filt=imfilter(fB,h);ReconstructthefilteredRGBimage

>>f_filtered=cat(3,fR_filt,fG_filt,fB_filt);Or,performtheentireoperationinavectorfashionontheRGBimage

>>f_filtered=imfilter(f,h);EECE-5626:ColorImageProcessing21/24ColorImageSmoothing(4)Example:SmoothinginRGBspaceversusHSVspacef=imread([imagedir,'Fig0604(a)(iris).tif']);imshow(f);h=fspecial('average',11);g=imfilter(f,h,'same');figure;imshow(g);w=rgb2hsv(f);w=imfilter(w,h,'same');w=hsv2rgb(w);figure;imshow(w);Or