high dynamic range imaging. camera pipeline 12 bits8 bits
TRANSCRIPT
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High dynamic range imaging
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Camera pipeline
12 bits 8 bits
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Short exposure
10-6 106
10-6 106
Real worldradiance
Pictureintensity
dynamic range
Pixel value 0 to 255
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Long exposure
10-6 106
10-6 106
Real worldradiance
Pictureintensity
dynamic range
Pixel value 0 to 255
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Varying shutter speeds
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Recovering High Dynamic Range Radiance Maps
from PhotographsPaul E. Debevec Jitendra Malik
SIGGRAPH 1997
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Recovering response curve
12 bits 8 bits
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Dt =1/4 sec
Dt =1 sec
Dt =1/8 sec
Dt =2 sec
Image series
Dt =1/2 sec
Recovering response curve
• 1• 1
• 1• 1
• 1• 1
• 1• 1
• 1• 1
• 3• 3
• 3• 3
• 3• 3
• 3• 3
• 3• 3
• 2• 2
• 2• 2
• 2• 2
• 2• 2
• 2• 2
0
255
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Idea behind the math
ln2
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Idea behind the math
Each line for a scene point.The offset is essentially determined by the unknown Ei
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Idea behind the math
Note that there is a shift that we can’t recover
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Math for recovering response curve
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Recovering response curve
• The solution can be only up to a scale, add a constraint
• Add a hat weighting function
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Recovered response function
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Constructing HDR radiance map
combine pixels to reduce noise and obtain a more reliable estimation
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Reconstructed radiance map
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Gradient Domain High Dynamic Range Compression
Raanan Fattal Dani Lischinski Michael Werman
SIGGRAPH 2002
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The method in 1D
log derivative
atte
nuat
e
integrateexp
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The method in 2D
• Given: a log-luminance image H(x,y)• Compute an attenuation map
• Compute an attenuated gradient field G:
• Problem: G may not be integrable!
H
HyxHyxG ),(),(
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Solution
• Look for image I with gradient closest to G in the least squares sense.
• I minimizes the integral:
22
2,
yx Gy
IG
x
IGIGIF
dxdyGIF ,
y
G
x
G
y
I
x
I yx
2
2
2
2Poissonequation
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Attenuation
gradient magnitudelog(Luminance) attenuation map
1),(
),(
yxHyx k
kH 1.0
8.0~
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Multiscale gradient attenuation
interpolate
interpolate
X =
X =
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Bilateral[Durand et al.]
Photographic[Reinhard et al.]
Gradient domain[Fattal et al.]
Informal comparison
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Informal comparison
Bilateral[Durand et al.]
Photographic[Reinhard et al.]
Gradient domain[Fattal et al.]
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Bilateral[Durand et al.]
Photographic[Reinhard et al.]
Gradient domain[Fattal et al.]
Informal comparison
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Local Laplacian Filters :Edge-aware Image
Processingwith a Laplacian PyramidSylvain Paris Samuel W. Hasinoff Jan
KautzSIGGRAPH 2011
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Background on Gaussian Pyramids• Resolution halved at each level using
Gaussian kernel
level 0
level 1
level 2level 3(residual)
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Background on Laplacian Pyramids• Difference between adjacent Gaussian
levels
level 0
level 1
level 2level 3(residual)
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Discontinuity
Intuition for 1D Edge
= + +
Input signal Texture Smooth
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• Decomposition for the sake of analysis only– We do not compute it in practice
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Discontinuity
Intuition for 1D Edge
= + +
Input signal Texture SmoothDoes not
contribute toLap. pyramidat that scale(d2/dx2=0)
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Discontinuity
Ideal Texture Increase
Texture
Keep unchanged
Amplify
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Our Texture Increase
“Locally good”version
Input signal
σ σ
σ
σ
user-defined parameter σ defines texture vs. edges
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Local nonlinearity
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DiscontinuityUnaffected
Our Texture Increase
= + +
“Locally good”Only left side
is affected
TextureLeft side is ok,right side is not
SmoothDoes not
contribute toLap. pyramidat that scale(d2/dx2=0)
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= + +
SmoothDoes not
contribute toLap. pyramidat that scale(d2/dx2=0)
Discussion
Negligible because
collocated with discontinuity
Negligible because
Gaussian kernel ≈ 0
DiscontinuityUnaffected
“Locally good”Only left side
is affected
TextureLeft side is ok,right side is not
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Good approximation to ideal case overall
(formal treatment in
paper)
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Texture ManipulationInput
35
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Texture ManipulationDecrease
36
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Texture ManipulationSmall Increase
37
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Texture ManipulationLarge Increase
38
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Texture ManipulationInput
39
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Texture ManipulationLarge Increase
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