elements of computer graphics - kaistvclab.kaist.ac.kr/cs580/slide13-hdr.pdf · 2018-07-02 ·...
TRANSCRIPT
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MinH.Kim KAISTCS580ComputerGraphics
CS580:ComputerGraphics
MinH.KimKAISTSchoolofComputing
MinH.Kim KAISTCS580ComputerGraphics
ElementsofComputerGraphics
2Rendering
MaterialmodelGeometry Light
Virtualphotography
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MinH.Kim KAISTCS580ComputerGraphics
HDRENVIRONMENTMAP
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MinH.Kim KAISTCS580ComputerGraphics
CapturingEnvironmentMaps• Photographingalightprobeproducesanenvironmentmaprepresentingincidentradiancefromalldirections.
4 B(w) = 2( w ⋅ n)n − w
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MinH.Kim KAISTCS580ComputerGraphics
CapturingEnvironmentMaps
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• Howtoremovethecamerafromtheenvironmentmap?
MinH.Kim KAISTCS580ComputerGraphics
SphericalEnvironmentMap&CubeMap• Galileo’sTomb
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http://www.pauldebevec.com/Probes/
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MinH.Kim KAISTCS580ComputerGraphics
Environmentcubemaps• Texturescanalsobeusedtomodeltheenvironmentinthedistancearoundtheobjectbeingrendered.
• Inthiscase,wetypicallyuse6squaretexturesrepresentingthefacesofalargecubesurroundingthescene.
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MinH.Kim KAISTCS580ComputerGraphics
Environmentcubemaps• Eachtexturepixelrepresentsthecolorasseenalongonedirectionintheenvironment.
• Thisiscalledacubemap.GLSLprovidesacube-texturedatatype,samplerCubespecificallyforthispurpose.
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MinH.Kim KAISTCS580ComputerGraphics
Environmentcubemaps• Duringtheshadingofapoint,wecantreatthematerialatthatpointasaperfectmirrorandfetchtheenvironmentdatafromtheappropriateincomingdirection.
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MinH.Kim KAISTCS580ComputerGraphics
Environmentmapshader• Wecalculateinthepreviouslecture.• Thisbouncedvectorwillpointpointstowardstheenvironmentdirection,whichwouldbeobservedinamirroredsurface.
• Bylookingupthecubemap,usingthisdirection,wegivethesurfacetheappearanceofamirror.
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B(v)
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MinH.Kim KAISTCS580ComputerGraphics
Environmentmapshader• Fragmentshader
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#version130uniformsampler2DuTexUnit0;invec3nNormal;invec4vPosition;outvec4fragColor;vec3reflect(vec3w,vec3n){returnn*(dot(w,n)*2.0)-w;//bouncevector}voidmain(){vec3normal=normalize(vNormal);vec3reflected=reflect(normalize(vec3(-vPosition)),normal);vec4texColor0=textureCube(uTexUnit0,reflected);fragColor=vec4(texColor0.r,texColor0.g,texColor0.b,1.0);;}
[0,0,0,1]t
MinH.Kim KAISTCS580ComputerGraphics
Environmentmapshader• -vPositionrepresentstheviewvector• textureCubeisaspecialGLSLfunctionthattakesadirectionvectorandreturnsthecolorstoredatthisdirectioninthecubetexturemap.
• Hereweassumeeye-coordinates,butframechangesmaybeneeded.
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v
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MinH.Kim KAISTCS580ComputerGraphics
Environmentmapshader• Thiscanbeusedforrefraction.
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MinH.Kim KAISTCS580ComputerGraphics
HIGH-DYNAMIC-RANGEIMAGING
MeasureRadianceas2DImages
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Acknowledgements:someofslidesarecourtesyofProf.PaulDebevec(USC),Prof.AlexeiEfros(CMU),andProf.FredoDurand(MIT)
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MinH.Kim KAISTCS580ComputerGraphics
TheProblem
• Limitofdigitalcamera
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MinH.Kim KAISTCS580ComputerGraphics
Problem:DynamicRange
• Therealworldishighdynamicrange
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1 1,500
2,000,000,000400,000
25,000
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MinH.Kim KAISTCS580ComputerGraphics
10-6 106
10-6 106
Real world
Picture
Low contrast
High dynamic range
Multipleexposurephotography
17CourtesyofF.Durand
MinH.Kim KAISTCS580ComputerGraphics
10-6 106
10-6 106
Real world
Picture
Low contrast
High dynamic range
Multipleexposurephotography
18CourtesyofF.Durand
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MinH.Kim KAISTCS580ComputerGraphics
10-6 106
10-6 106
Real world
Picture
Low contrast
High dynamic range
Multipleexposurephotography
19CourtesyofF.Durand
MinH.Kim KAISTCS580ComputerGraphics
10-6 106
10-6 106
Real world
Picture
Low contrast
High dynamic range
Multipleexposurephotography
20CourtesyofF.Durand
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MinH.Kim KAISTCS580ComputerGraphics
10-6 106
10-6 106
Real world
Picture
Low contrast
High dynamic range
Multipleexposurephotography
21CourtesyofF.Durand
MinH.Kim KAISTCS580ComputerGraphics
Howdowevaryexposure?• Options:– Shutterspeed– Aperture– ISO– Neutraldensityfilter
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MinH.Kim KAISTCS580ComputerGraphics
Tradeoffs• Shutterspeed– Range:~30secto1/4000sec(6ordersofmagnitude)– Pros:reliable,linear– Cons:sometimesnoiseforlongexposure
• Aperture– Range:~f/1.4tof/22(2.5ordersofmagnitude)– Cons:changesdepthoffield– Usefulwhendesperate
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MinH.Kim KAISTCS580ComputerGraphics
Tradeoffs• ISO– Range:~100to1600(1.5ordersofmagnitude)
– Cons:noise– Usefulwhendesperate
• Neutraldensityfilter– Range:upto4densities(4ordersofmagnitude)&canbestacked
– Cons:notperfectlyneutral(colorshift),– notveryprecise,needtotouchcamera(shake)– Pros:workswithstrobe/flash,goodcomplementwhendesperate
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MinH.Kim KAISTCS580ComputerGraphics
HDRimageusingmultipleexposure
• GivenNphotosatdifferentexposure• RecoveranHDRcolorforeachpixel
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MinH.Kim KAISTCS580ComputerGraphics
Ifweknowtheresponsecurve
• Justlookuptheinverseoftheresponsecurve• Buthowdowegetthecurve?
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Pixel value
scene value
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MinH.Kim KAISTCS580ComputerGraphics
Calibratingtheresponsecurve• Twobasicsolutions– Varysceneluminanceandseepixelvalues
• Assumeswecontrolandknowsceneluminance
– Varyexposureandseepixelvalueforonesceneluminance• Butnotethatwecanusuallynotvaryexposuremorefinelythanby1/3stop
• Bestofboth:– Varyexposure– Exploitthelargenumberofpixels
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MinH.Kim KAISTCS580ComputerGraphics
TheAlgorithm• Imageseries
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• 3
• 1 •
2
t = 1/100 sec
• 3
• 1 •
2
t = 1 sec
• 3
• 1 • 2
t = 1/1000 sec
• 3
• 1 •
2
t = 10 sec
• 3
• 1 •
2
t = 1/10 sec
Z = f (H )H = E ⋅ Δtlog(H )= log(E)+ log(Δt)where Z is pixel value, H is exposure,E is radiance, Δt is exposure time.
CourtesyofPaulDebevec
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MinH.Kim KAISTCS580ComputerGraphics
Responsecurve
• Exposureisunknown,fittofindasmoothcurve
29log Exposure
curve
Pixe
l val
ue 3
1
2
log Exposure
Pixe
l val
ue
Assumingunitradianceforeachpixel
Afteradjustingradiancestoobtainasmoothresponse
MinH.Kim KAISTCS580ComputerGraphics
TheMath
• Letg(z)bethediscretelogarithmicinverseresponsefunction
• Foreachpixelsiteiineachimagej,want:
30g(Zij )= log(Ei )+ log(Δt j )
Z = f (H ) → logH = log f −1(Z ) → logH = g(Z )
Z = f (H )H = E ⋅ Δtlog(H )= log(E)+ log(Δt)where Z is pixel value, H is exposure,E is radiance, Δt is exposure time.
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TheMath• Solvetheoverdeterminedlinearsystem:
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log(Ei )+ log(Δt j )− g(Zij )⎡⎣ ⎤⎦2
j=1
P
∑ +λ g ''(z)2z=Zmin
Zmax
∑i=1
N
∑
Dataterm Regularizationterm
MinH.Kim KAISTCS580ComputerGraphics
TheMath• Howtoreconstructradiance:
• Weightingfactorhandlesthenoisearoundjointsofpieces
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logEi =
w(Zij ) g(Zij )− log(Δt j )( )j=1
P
∑
w(Zij )j=1
P
∑
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Matlabcodefunction[g,lE]=gsolve(Z,B,l,w) %Zispixelvalue,Bisexposurefactorn=256;A=zeros(size(Z,1)*size(Z,2)+n+1,n+size(Z,1));b=zeros(size(A,1),1);k=1; %Includethedata-fittingequationsfori=1:size(Z,1)forj=1:size(Z,2)wij=w(Z(i,j)+1);A(k,Z(i,j)+1)=wij;A(k,n+i)=-wij;b(k,1)=wij*B(i,j);k=k+1;endendA(k,129)=1; %Fixthecurvebysettingitsmiddlevalueto0k=k+1;fori=1:n-2 %IncludethesmoothnessequationA(k,i)=l*w(i+1);A(k,i+1)=-2*l*w(i+1);A(k,i+2)=l*w(i+1);k=k+1;Endx=A\b; %SolvethesystemusingSVDg=x(1:n);lE=x(n+1:size(x,1));
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CourtesyofPaulDebevec
MinH.Kim KAISTCS580ComputerGraphics
Result:digitalcamera• Reconstructedcameraresponse
34logExposure
Pixelvalue
RecoveredresponsecurveKodakDCS4601/30to30sec
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MinH.Kim KAISTCS580ComputerGraphics
Reconstructedradiancemap
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CourtesyofP
aulD
ebev
ec
MinH.Kim KAISTCS580ComputerGraphics
Result:colorfilm• KodakGold,ASA100
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MinH.Kim KAISTCS580ComputerGraphics
Recoveredresponsecurves
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Red Green
Blue RGB
MinH.Kim KAISTCS580ComputerGraphics
TheRadianceMap• Measuresceneradiancebyusingdigitalcamera
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MinH.Kim KAISTCS580ComputerGraphics
TheRadianceMap
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Linearlyscaledtodisplaydevice
MinH.Kim KAISTCS580ComputerGraphics
HDRimageprocessing
• Importantalsofordepthoffieldpost-process
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Motionblurappliedtolow-dynamic-rangepicture
Motionblurappliedtohigh-dynamic-rangepicture
Realmotion-blurredpicture
ImagesfromDebevec&Malik1997
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MinH.Kim KAISTCS580ComputerGraphics
SmarterHDRcapture• Automaticexposurealignment• Ghostremoval• Lensflareremoval• ImplementingHDRincameras
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ImagesGregWard
MinH.Kim KAISTCS580ComputerGraphics
Imageregistration
• Howtorobustlycompareimagesofdifferentexposure?
• Useablackandwhiteversionoftheimagethresholdedatthemedian– Median-ThresholdBitmap(MTB)
• Findthetranslationthatminimizesdifference
• Accelerateusingpyramid
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MinH.Kim KAISTCS580ComputerGraphics
Alignmentresult
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MinH.Kim KAISTCS580ComputerGraphics
HDRVideo
• GenerateHDRvideobyrapidlyvaryingtheexposureofeachframe(automaticexposurecontrolandstitchingneighboringframes)
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Kangetal.SIG2003
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MinH.Kim KAISTCS580ComputerGraphics
Hardwaresolutions
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MinH.Kim KAISTCS580ComputerGraphics
HDRImagingHardware
• Mosaicneutral-densityfilterforspatialvaryingexposureimaging.Fourdifferentexposuresofneutraldensityfilterareinstalledinfrontofthedetectorarray.Thedifferencebetweenneutraldensityise3=4e2=16e1=64e0.NayarandMitsunaga[2000].
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MinH.Kim KAISTCS580ComputerGraphics
Fileformats• PortableFloatMap(.pfm)– 4bytes(4x8=32bits)perchannel– 12bytesperpixel(=4bytesx3ch.)– TextheadersimilartoJeffPoskanzer’s.ppmimageformat(FloatingPointTIFFsimilar):
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MinH.Kim KAISTCS580ComputerGraphics
Fileformats
• Radianceformat(.pic,.hdr)– 4bytes(4x8=32bits)perpixel– (145,215,87,149)=(145,215,87)*2^(149-128)(1190000,1760000,713000)
– (145,215,87,103)=(145,215,87)*2^(103-128)(0.00000432,0.00000641,0.00000259)
48Ward,Greg."RealPixels,"inGraphicsGemsIV,editedbyJamesArvo,AcademicPress,1994
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MinH.Kim KAISTCS580ComputerGraphics
Fileformats
• ILM’sOpenEXR(.exr)– 2bytes(2x8=16bits)perchannel(2x3=6bytes(16x3bits)perpixel)
– Losslesscompressionsupport– Half-precisionfloat– 65504(maxhalfprecision)– http://www.openexr.net/– Multi-channelsupported
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MinH.Kim KAISTCS580ComputerGraphics
SunnybrookHDRdisplay• Usebrightsource+two8-bitmodulators• Transmissionmultipliestogether• Over10,000:1dynamicrangepossible
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MinH.Kim KAISTCS580ComputerGraphics
HighDynamicRangeDisplay
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MinH.Kim KAISTCS580ComputerGraphics
Howitworks
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MinH.Kim KAISTCS580ComputerGraphics
WhatifedgecontrastexceedsLCDrange?• Observerscannottellwhenthishappensbecausetheeyehaslimitedlocalcontrastcapacityduetoscattering
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MinH.Kim KAISTCS580ComputerGraphics
HDRDisplay
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BrightSideDR37-P(nowDolby)
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MinH.Kim KAISTCS580ComputerGraphics
Displaytheinformation• Matchlimitedcontrastofthemedium• Preservedetails
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10-6 106
10-6 106
Real world
Picture
Low contrast
High dynamic range
MinH.Kim KAISTCS580ComputerGraphics
Insightoftonemapping
• TumblinandRushmeier(1993)’stone-reproductionoperatorcomprisesreal-worldobservations,inversedisplayobservations,andaninversedisplaydevicefunctionthatachievesaperceptualmatchbetweenreal-worldobservationandtheobservationofthereproducedimageonthedisplay.
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MinH.Kim KAISTCS580ComputerGraphics
Howhumansdealwithdynamicrange?
• We'resensitivetocontrast(multiplicative)– Aratioof1:2isperceivedasthesamecontrastasaratioof100to200
– Makessensebecauseilluminationhasamultiplicativeeffect– Usethelogdomainasmuchaspossible
• Dynamicadaptation(verylocalinretina)– Pupil– Neural/chemical
• Differentsensitivitytospatialfrequencies
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MinH.Kim KAISTCS580ComputerGraphics
ContrastSensitivity• SineWavegrating• Whatcontrastisnecessarytomakethegratingvisible?
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MinH.Kim KAISTCS580ComputerGraphics
ContrastSensitivityFunction(CSF)
• CampbellRobsoncontrast-sensitivitychart
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loga
rithm
icdecreaseofcon
trast
logarithmicincreaseofspatialfrequency
MinH.Kim KAISTCS580ComputerGraphics
ContrastSensitivityFunction(CSF)
• Lowsensitivitytolowfrequencies
• Importanceofmediumtohighfrequencies
• Mostmethodstodealwithdynamicrange,reducingthecontrastoflowfrequencies
• Butkeepthecolor
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MinH.Kim KAISTCS580ComputerGraphics
Contrastreduction• Input:high-dynamic-rangeimage– Floatingpointperpixel
61 CourtesyofF
redo
Duran
d
MinH.Kim KAISTCS580ComputerGraphics
Naïvetechnique• Scenehas1:10,000contrast,displayhas1:100• Simplestcontrastreduction?
62 CourtesyofF
redo
Duran
d
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Naïve:Gammacompression
• XàXϒ(whereϒ=0.45inthiscase)• But...colorsarewashed-out.Why?
63 CourtesyofF
redo
Duran
d
MinH.Kim KAISTCS580ComputerGraphics
Gammacompressiononintensity
• ColorsareOK,butdetails(intensityhigh-frequency)areblurred
64 CourtesyofF
redo
Duran
d
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Oppenheim1968,Chiuetal.1993
• Reducecontrastoflow-frequencies• Keephighfrequencies
65 CourtesyofF
redo
Duran
d
MinH.Kim KAISTCS580ComputerGraphics
Thehaloartifact
• Forstrongedges• Becausetheycontainhighfrequency
66 CourtesyofF
redo
Duran
d
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MinH.Kim KAISTCS580ComputerGraphics
Whyhaloartifacthappen
67
MinH.Kim KAISTCS580ComputerGraphics
DurandBilateralFiltering• Donotbluracrossedges• Non-linearfiltering
68 CourtesyofF
redo
Duran
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MinH.Kim KAISTCS580ComputerGraphics
StartwithGaussianfiltering
69CourtesyofFredoDurand
MinH.Kim KAISTCS580ComputerGraphics
StartwithGaussianfiltering
70CourtesyofFredoDurand
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MinH.Kim KAISTCS580ComputerGraphics
StartwithGaussianfiltering
71CourtesyofFredoDurand
MinH.Kim KAISTCS580ComputerGraphics
Gaussianfilterasweightedaverage
72CourtesyofFredoDurand
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MinH.Kim KAISTCS580ComputerGraphics
Theproblemofedges
73CourtesyofFredoDurand
MinH.Kim KAISTCS580ComputerGraphics
PrincipleofBilateralfiltering
74CourtesyofFredoDurand
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MinH.Kim KAISTCS580ComputerGraphics
Bilateralfiltering
75CourtesyofFredoDurand
MinH.Kim KAISTCS580ComputerGraphics
Bilateralfiltering
76CourtesyofFredoDurand
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MinH.Kim KAISTCS580ComputerGraphics
Normalizationfactor
77CourtesyofFredoDurand
MinH.Kim KAISTCS580ComputerGraphics
Bilateralfilteringisnon-linear
78CourtesyofFredoDurand
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MinH.Kim KAISTCS580ComputerGraphics
OtherView
79CourtesyofFredoDurand
MinH.Kim KAISTCS580ComputerGraphics
Handlinguncertainty
80CourtesyofFredoDurand
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MinH.Kim KAISTCS580ComputerGraphics
DurandandDorsey(SIG2002)• FollowingOppenheimetal,fromtheinputimageand
illuminanceimage,thereflectanceimagecouldbereconstructedbydividingtheinputandilluminanceimage.
• Thesmoothilluminationiscalled“baselayer”,whereastheresultofthedivisioniscalled“detaillayer”
• CompressionisdoneinthelogarithmicchannelsoftheluminancechannelintheYxycolorspace
81
MinH.Kim KAISTCS580ComputerGraphics
Durand’scontrastreduction
82DurandandDorsey(SIG2002)
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MinH.Kim KAISTCS580ComputerGraphics
Durand’scontrastreduction
83DurandandDorsey(SIG2002)
MinH.Kim KAISTCS580ComputerGraphics
Durand’scontrastreduction
84DurandandDorsey(SIG2002)
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MinH.Kim KAISTCS580ComputerGraphics
Durand’scontrastreduction
85DurandandDorsey(SIG2002)
MinH.Kim KAISTCS580ComputerGraphics
Durand’scontrastreduction
86DurandandDorsey(SIG2002)
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MinH.Kim KAISTCS580ComputerGraphics
Durand’scontrastreduction
87DurandandDorsey(SIG2002)
MinH.Kim KAISTCS580ComputerGraphics
Durand’scontrastreduction
88DurandandDorsey(SIG2002)