image blending and compositing - computer...
TRANSCRIPT
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Image Blending and Compositing
15-463: Computational PhotographyAlexei Efros, CMU, Fall 2011
© NASA
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Image Compositing
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Compositing Procedure1. Extract Sprites (e.g using Intelligent Scissors in Photoshop)
Composite by David Dewey
2. Blend them into the composite (in the right order)
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Need blending
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Alpha Blending / Feathering
01
01
+
=Iblend = αIleft + (1-α)Iright
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Affect of Window Size
0
1 left
right0
1
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Affect of Window Size
0
1
0
1
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Good Window Size
0
1
“Optimal” Window: smooth but not ghosted
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What is the Optimal Window?
To avoid seams• window = size of largest prominent feature
To avoid ghosting• window <= 2*size of smallest prominent feature
Natural to cast this in the Fourier domain• largest frequency <= 2*size of smallest frequency• image frequency content should occupy one “octave” (power of two)
FFT
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What if the Frequency Spread is Wide
Idea (Burt and Adelson)• Compute Fleft = FFT(Ileft), Fright = FFT(Iright)• Decompose Fourier image into octaves (bands)
– Fleft = Fleft1 + Fleft
2 + …
• Feather corresponding octaves Flefti with Fright
i
– Can compute inverse FFT and feather in spatial domain
• Sum feathered octave images in frequency domain
Better implemented in spatial domain
FFT
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Octaves in the Spatial Domain
Bandpass Images
Lowpass Images
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Pyramid Blending
0
1
0
1
0
1
Left pyramid Right pyramidblend
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Pyramid Blending
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laplacianlevel
4
laplacianlevel
2
laplacianlevel
0
left pyramid right pyramid blended pyramid
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Laplacian Pyramid: Blending
General Approach:1. Build Laplacian pyramids LA and LB from images A and B2. Build a Gaussian pyramid GR from selected region R3. Form a combined pyramid LS from LA and LB using nodes
of GR as weights:• LS(i,j) = GR(I,j,)*LA(I,j) + (1-GR(I,j))*LB(I,j)
4. Collapse the LS pyramid to get the final blended image
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Blending Regions
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Horror Photo
© david dmartin (Boston College)
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Results from this class (fall 2005)
© Chris Cameron
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Season Blending (St. Petersburg)
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Season Blending (St. Petersburg)
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Simplification: Two-band Blending
Brown & Lowe, 2003• Only use two bands: high freq. and low freq.• Blends low freq. smoothly• Blend high freq. with no smoothing: use binary alpha
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Low frequency (λ > 2 pixels)
High frequency (λ < 2 pixels)
2-band Blending
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Linear Blending
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2-band Blending
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Don’t blend, CUT!
So far we only tried to blend between two images. What about finding an optimal seam?
Moving objects become ghosts
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Davis, 1998
Segment the mosaic• Single source image per segment• Avoid artifacts along boundries
– Dijkstra’s algorithm
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min. error boundary
Minimal error boundary
overlapping blocks vertical boundary
__ ==
22
overlap error
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Seam Carving
http://www.youtube.com/watch?v=6NcIJXTlugc
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Graphcuts
What if we want similar “cut-where-things-agree” idea, but for closed regions?• Dynamic programming can’t handle loops
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Graph cuts – a more general solution
n-links
s
t a cuthard constraint
hard constraint
Minimum cost cut can be computed in polynomial time
(max-flow/min-cut algorithms)
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Kwatra et al, 2003
Actually, for this example, DP will work just as well…
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Lazy Snapping
Interactive segmentation using graphcuts
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Gradient Domain
In Pyramid Blending, we decomposed our image into 2nd derivatives (Laplacian) and a low-res image
Let us now look at 1st derivatives (gradients):• No need for low-res image
– captures everything (up to a constant)
• Idea: – Differentiate– Blend / edit / whatever
– Reintegrate
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Gradient Domain blending (1D)
Twosignals
Regularblending
Blendingderivatives
bright
dark
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Gradient Domain Blending (2D)
Trickier in 2D:• Take partial derivatives dx and dy (the gradient field)• Fidle around with them (smooth, blend, feather, etc)• Reintegrate
– But now integral(dx) might not equal integral(dy)
• Find the most agreeable solution– Equivalent to solving Poisson equation
– Can be done using least-squares
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Perez et al., 2003
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Perez et al, 2003
Limitations:• Can’t do contrast reversal (gray on black -> gray on white)• Colored backgrounds “bleed through”• Images need to be very well aligned
editing
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Gradients vs. Pixels
Can we use this for range compression?
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White?
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White?
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White?
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Thinking in Gradient Domain
Our very own Jim McCann::James McCann
Real-Time Gradient-Domain Painting, SIGGRAPH 2009
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Gradient Domain as Image Representation
See GradientShop paper as good example:
http://www.gradientshop.com/
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� Can be used to exert high-level control over images
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� Can be used to exert high-level control over images� gradients – low level image-features
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� Can be used to exert high-level control over images� gradients – low level image-features
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� Can be used to exert high-level control over images� gradients – low level image-features
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� Can be used to exert high-level control over images� gradients – low level image-features
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� Can be used to exert high-level control over images� gradients – low level image-features
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� Can be used to exert high-level control over images� gradients – low level image-features
� gradients – give rise to high level image-features
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� Can be used to exert high-level control over images� gradients – low level image-features
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� Can be used to exert high-level control over images� gradients – low level image-features
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� manipulate local gradients to manipulate global image interpretation
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pixelgradient
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� Can be used to exert high-level control over images� gradients – low level image-features
� gradients – give rise to high level image-features
� manipulate local gradients to manipulate global image interpretation
+0
+0
+0
+0
+0
pixelgradient
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� Can be used to exert high-level control over images� gradients – low level image-features
� gradients – give rise to high level image-features
� manipulate local gradients to manipulate global image interpretation
+0
+0
+0
+0
+0
pixelgradient
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� Can be used to exert high-level control over images� gradients – give rise to high level image-features
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� Can be used to exert high-level control over images� gradients – give rise to high level image-features
� Edges
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� Can be used to exert high-level control over images� gradients – give rise to high level image-features
� Edges� object boundaries
� depth discontinuities
� shadows
� …
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� Can be used to exert high-level control over images� gradients – give rise to high level image-features
� Edges
� Texture
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� Can be used to exert high-level control over images� gradients – give rise to high level image-features
� Edges
� Texture� visual richness
� surface properties
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� Can be used to exert high-level control over images� gradients – give rise to high level image-features
� Edges
� Texture
� Shading
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� Can be used to exert high-level control over images� gradients – give rise to high level image-features
� Edges
� Texture
� Shading� lighting
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� Can be used to exert high-level control over images� gradients – give rise to high level image-features
� Edges
� Texture
� Shading� lighting
� shape
sculpting the faceusing shading (makeup)
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� Can be used to exert high-level control over images� gradients – give rise to high level image-features
� Edges
� Texture
� Shading� lighting
� shape
sculpting the faceusing shading (makeup)
sculpting the faceusing shading (makeup)
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� Can be used to exert high-level control over images� gradients – give rise to high level image-features
� Edges
� Texture
� Shading� lighting
� shape
sculpting the faceusing shading (makeup)
sculpting the faceusing shading (makeup)
sculpting the faceusing shading (makeup)
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� Can be used to exert high-level control over images� gradients – give rise to high level image-features
� Edges
� Texture
� Shading� lighting
� shape
sculpting the faceusing shading (makeup)
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� Can be used to exert high-level control over images� gradients – give rise to high level image-features
� Edges
� Texture
� Shading
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� Can be used to exert high-level control over images� gradients – give rise to high level image-features
� Edges
� Texture
� Shading
� Artifacts
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� Can be used to exert high-level control over images� gradients – give rise to high level image-features
� Edges
� Texture
� Shading
� Artifacts� noise
sensor noise
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� Can be used to exert high-level control over images� gradients – give rise to high level image-features
� Edges
� Texture
� Shading
� Artifacts� noise
� seams
seams incomposite images
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� Can be used to exert high-level control over images� gradients – give rise to high level image-features
� Edges
� Texture
� Shading
� Artifacts� noise
� seams
� compressionartifacts
blocking incompressed images
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� Can be used to exert high-level control over images� gradients – give rise to high level image-features
� Edges
� Texture
� Shading
� Artifacts� noise
� seams
� compressionartifacts
ringing incompressed images
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� Can be used to exert high-level control over images
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� Optimization framework
Pravin Bhat et al
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� Optimization framework� Input unfiltered image – u
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� Optimization framework� Input unfiltered image – u� Output filtered image – f
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� Optimization framework� Input unfiltered image – u� Output filtered image – f� Specify desired pixel-differences – (gx, gy)
min (fx – gx)2 + (fy – gy)2
f
Energy function
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� Optimization framework� Input unfiltered image – u� Output filtered image – f� Specify desired pixel-differences – (gx, gy)� Specify desired pixel-values – d
min (fx – gx)2 + (fy – gy)2 + (f – d)2
f
Energy function
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� Optimization framework� Input unfiltered image – u� Output filtered image – f� Specify desired pixel-differences – (gx, gy)� Specify desired pixel-values – d� Specify constraints weights – (wx, wy, wd)
min wx(fx – gx)2 + wy(fy – gy)2 + wd(f – d)2
f
Energy function
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Pseudo image relighting� change scene illumination
in post-production
� example
input
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Pseudo image relighting� change scene illumination
in post-production
� example
manual relight
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Pseudo image relighting� change scene illumination
in post-production
� example
input
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Pseudo image relighting� change scene illumination
in post-production
� example
GradientShop relight
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Pseudo image relighting� change scene illumination
in post-production
� example
GradientShop relight
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Pseudo image relighting� change scene illumination
in post-production
� example
GradientShop relight
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Pseudo image relighting� change scene illumination
in post-production
� example
GradientShop relight
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Pseudo image relighting
u
f
o
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Pseudo image relighting
min wx(fx – gx)2 + f wy(fy – gy)2 +
wd(f – d)2
Energy function
u
f
o
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Pseudo image relighting
min wx(fx – gx)2 + f wy(fy – gy)2 +
wd(f – d)2
Energy function
� Definition:� d = u
u
f
o
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Pseudo image relighting
min wx(fx – gx)2 + f wy(fy – gy)2 +
wd(f – d)2
Energy function
u
f
o� Definition:� d = u� gx(p) = ux(p) * (1 + a(p))� a(p) = max(0, - u(p).o(p))
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Pseudo image relighting
min wx(fx – gx)2 + f wy(fy – gy)2 +
wd(f – d)2
Energy function
u
f
o� Definition:� d = u� gx(p) = ux(p) * (1 + a(p))� a(p) = max(0, - u(p).o(p))
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Sparse data interpolation� Interpolate scattered data
over images/video
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Sparse data interpolation� Interpolate scattered data
over images/video
� Example app: Colorization*
input output
*Levin et al. – SIGRAPH 2004
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u
f
user data
Sparse data interpolation
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u
f
user data
Sparse data interpolation
min wx(fx – gx)2 + f wy(fy – gy)2 +
wd(f – d)2
Energy function
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u
f
user data
Sparse data interpolation
min wx(fx – gx)2 + f wy(fy – gy)2 +
wd(f – d)2
Energy function
� Definition:� d = user_data
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u
f
user data
Sparse data interpolation
min wx(fx – gx)2 + f wy(fy – gy)2 +
wd(f – d)2
Energy function
� Definition:� d = user_data� if user_data(p) defined
wd(p) = 1else
wd(p) = 0
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u
f
user data
Sparse data interpolation
min wx(fx – gx)2 + f wy(fy – gy)2 +
wd(f – d)2
Energy function
� Definition:� d = user_data� if user_data(p) defined
wd(p) = 1else
wd(p) = 0� gx(p) = 0; gy(p) = 0
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u
f
user data
Sparse data interpolation
min wx(fx – gx)2 + f wy(fy – gy)2 +
wd(f – d)2
Energy function
� Definition:� d = user_data� if user_data(p) defined
wd(p) = 1else
wd(p) = 0� gx(p) = 0; gy(p) = 0� wx(p) = 1/(1 + c*|ux(p)|)
wy(p) = 1/(1 + c*|uy(p)|)
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u
f
user data
Sparse data interpolation
min wx(fx – gx)2 + f wy(fy – gy)2 +
wd(f – d)2
Energy function
� Definition:� d = user_data� if user_data(p) defined
wd(p) = 1else
wd(p) = 0� gx(p) = 0; gy(p) = 0� wx(p) = 1/(1 + c*|ux(p)|)
wy(p) = 1/(1 + c*|uy(p)|)
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u
f
user data
min wx(fx – gx)2 + f wy(fy – gy)2 +
wd(f – d)2
Energy function
Sparse data interpolation
� Definition:� d = user_data� if user_data(p) defined
wd(p) = 1else
wd(p) = 0� gx(p) = 0; gy(p) = 0� wx(p) = 1/(1 + c*|el(p)|)
wy(p) = 1/(1 + c*|el(p)|)