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LCIS: A Boundary Hierarchy For Detail-Preserving Contrast Reduction. Jack Tumblin and Greg Turk Georgia Institute of Technology SIGGRAPH 1999 Presented by Rob Glaubius. Motivation. Detail visible almost everywhere in a scene Difficult to capture rich detail in high-contrast scenes - PowerPoint PPT Presentation
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LCIS: A Boundary Hierarchy For Detail-Preserving Contrast
Reduction
LCIS: A Boundary Hierarchy For Detail-Preserving Contrast
Reduction
Jack Tumblin and Greg TurkGeorgia Institute of Technology
SIGGRAPH 1999
Presented by Rob Glaubius
Jack Tumblin and Greg TurkGeorgia Institute of Technology
SIGGRAPH 1999
Presented by Rob Glaubius
MotivationMotivation
Detail visible almost everywhere in a scene Difficult to capture rich detail in high-
contrast scenes CRT contrast: 100:1 Target scene contrast: ~100,000:1
Detail visible almost everywhere in a scene Difficult to capture rich detail in high-
contrast scenes CRT contrast: 100:1 Target scene contrast: ~100,000:1
MotivationMotivation
Simple scene intensity adjustment
Id = F(m·Is)
Id: display intensity
Is: scene intensity
m: scale factor
: compression/expansion term
F: enforces boundary conditions
Simple scene intensity adjustment
Id = F(m·Is)
Id: display intensity
Is: scene intensity
m: scale factor
: compression/expansion term
F: enforces boundary conditions
LCIS - A PreviewLCIS - A Preview
“Mathematically mimic a well-known artistic technique for rendering high contrast scenes”
Coarse-to-fine rendering of boundaries and shading
“Mathematically mimic a well-known artistic technique for rendering high contrast scenes”
Coarse-to-fine rendering of boundaries and shading
LCIS - A PreviewLCIS - A Preview
Low Curvature Image Simplifier Hierarchy of sharp boundaries and smooth
shadings Goal - low contrast, highly detailed images
Low Curvature Image Simplifier Hierarchy of sharp boundaries and smooth
shadings Goal - low contrast, highly detailed images
LCIS vs. Linear Filter Hierarchies
LCIS vs. Linear Filter Hierarchies
Anisotropic DiffusionAnisotropic Diffusion
Treat intensity as heat fluid Temperature wants to flow from hot to cold
It = ·(C(x,y,t) I)• It : derivative of temperature change w.r.t. time• C : Conductivity
Constant conductivity repeated convolution with a Gaussian filter (isotropic diffusion)
Treat intensity as heat fluid Temperature wants to flow from hot to cold
It = ·(C(x,y,t) I)• It : derivative of temperature change w.r.t. time• C : Conductivity
Constant conductivity repeated convolution with a Gaussian filter (isotropic diffusion)
Anisotropic Diffusion, cont’dAnisotropic Diffusion, cont’d
Conductivity depends on image - as local “edginess” increases, conductivity decreases
C(x,y,t) = g(||I||)
where
g(m) = (1+(m/K)2)-1
K is a conductance threshold for m
Conductivity depends on image - as local “edginess” increases, conductivity decreases
C(x,y,t) = g(||I||)
where
g(m) = (1+(m/K)2)-1
K is a conductance threshold for m
Anisotropic Diffusion IllustratedAnisotropic Diffusion Illustrated
LCIS vs. Anisotropic DiffusionLCIS vs. Anisotropic Diffusion
LCIS - TheoryLCIS - Theory
3rd order derivatives instead of 2nd order Equalize curvature rather than intensity
It(x,y,t) = ·(C(x,y,t)F(x,y,t))
F: motive force from high to low curvature
F = (Ixxx + Iyyx, Ixxy + Iyyy)
C: Conductivity
C(x,y,t) = g(0.5(I2xx + I2
yy) + I2xy)
3rd order derivatives instead of 2nd order Equalize curvature rather than intensity
It(x,y,t) = ·(C(x,y,t)F(x,y,t))
F: motive force from high to low curvature
F = (Ixxx + Iyyx, Ixxy + Iyyy)
C: Conductivity
C(x,y,t) = g(0.5(I2xx + I2
yy) + I2xy)
LCIS - ImplementationLCIS - Implementation
Discrete images, so quantities are approximate, based on 4-connected neighbors and a constant time step
Discrete images, so quantities are approximate, based on 4-connected neighbors and a constant time step
LCIS HierarchyLCIS Hierarchy
Convert(Rin,Gin,Bin)
LCISK0 = 0
LCISK1
LCISK2
LCISK3
+ +
+
(Rout,Gout,Bout)
exp()
wcolor w0 w1 w2 w3
log(L)
log(R/L
,G/L
,B/L
)