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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 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

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LCIS: A Boundary Hierarchy For Detail-Preserving Contrast Reduction

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  1. LCIS: A Boundary Hierarchy For Detail-Preserving Contrast Reduction Jack Tumblin and Greg Turk Georgia Institute of Technology SIGGRAPH 1999 Presented by Rob Glaubius

  2. Motivation • 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

  3. Motivation • Simple scene intensity adjustment Id = F(m·Is) Id: display intensity Is: scene intensity m: scale factor  : compression/expansion term F: enforces boundary conditions

  4. LCIS - A Preview • “Mathematically mimic a well-known artistic technique for rendering high contrast scenes” • Coarse-to-fine rendering of boundaries and shading

  5. LCIS - A Preview • Low Curvature Image Simplifier • Hierarchy of sharp boundaries and smooth shadings • Goal - low contrast, highly detailed images

  6. LCIS vs. Linear Filter Hierarchies

  7. Anisotropic 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)

  8. Anisotropic 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

  9. Anisotropic Diffusion Illustrated

  10. LCIS vs. Anisotropic Diffusion

  11. LCIS - 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 + I2yy) + I2xy)

  12. LCIS - Implementation • Discrete images, so quantities are approximate, based on 4-connected neighbors and a constant time step

  13. LCIS K0 = 0 LCIS K1 LCIS K2 LCIS K3    wcolor w0 w1 w2 w3      + + (Rout,Gout,Bout) + exp() LCIS Hierarchy log(L) Convert (Rin,Gin,Bin) log(R/L,G/L,B/L)

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