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Multiscale Analysis of Images. Gilad Lerman Math 5467 (stealing slides from Gonzalez & Woods, and Efros). The Multiscale Nature of Images. Recall: Gaussian pre-filtering. G 1/8. G 1/4. Gaussian 1/2. Solution: filter the image, then subsample - PowerPoint PPT Presentation
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Multiscale Analysis of Images
Gilad LermanMath 5467
(stealing slides from Gonzalez & Woods, and Efros)
The Multiscale Nature of Images
Recall: Gaussian pre-filtering
G 1/4
G 1/8
Gaussian 1/2
Solution: filter the image, then subsample• Filter size should double for each ½ size reduction.
Subsampling with Gaussian pre-filtering
G 1/4 G 1/8Gaussian 1/2
Solution: filter the image, then subsample• Filter size should double for each ½ size reduction.
Image Pyramids
Known as a Gaussian Pyramid [Burt and Adelson, 1983]• In computer graphics, a mip map [Williams, 1983]• A precursor to wavelet transform
A bar in the big images is a hair on the zebra’s nose; in smaller images, a stripe; in the smallest, the animal’s nose
Figure from David Forsyth
Gaussian pyramid construction
filter mask
Repeat• Filter• Subsample
Until minimum resolution reached • can specify desired number of levels (e.g., 3-level pyramid)
The whole pyramid is only 4/3 the size of the original image!
What does blurring take away? (recall)
original
What does blurring take away? (recall)
smoothed (5x5 Gaussian)
High-Pass filter
smoothed – original
Band-pass filtering
Laplacian Pyramid (subband images)Created from Gaussian pyramid by subtraction
Gaussian Pyramid (low-pass images)
Laplacian Pyramid
How can we reconstruct (collapse) this pyramid into the original image?
Need this!
Originalimage
Image resampling (interpolation)So far, we considered only power-of-two subsampling
• What about arbitrary scale reduction?• How can we increase the size of the image?
Recall how a digital image is formed
• It is a discrete point-sampling of a continuous function• If we could somehow reconstruct the original function, any new image could be generated, at any resolution and
scale
1 2 3 4 5
d = 1 in this example
Image resamplingSo far, we considered only power-of-two subsampling
• What about arbitrary scale reduction?• How can we increase the size of the image?
Recall how a digital image is formed
• It is a discrete point-sampling of a continuous function• If we could somehow reconstruct the original function, any new image could be generated, at any resolution and
scale
1 2 3 4 5
d = 1 in this example
Image resamplingSo what to do if we don’t know
1 2 3 4 52.5
1 d = 1 in this example
• Answer: guess an approximation• Can be done in a principled way: filtering
Image reconstruction• Convert to a continuous function
• Reconstruct by cross-correlation:
Resampling filtersWhat does the 2D version of this hat function look like?
Better filters give better resampled images• Bicubic is common choice
Why not use a Gaussian?
What if we don’t want whole f, but just one sample?
performs linear interpolation
(tent function) performs bilinear interpolation
Bilinear interpolationSmapling at f(x,y):
Applications (more efficient with wavelets)
•Coding High frequency coefficients need fewer bits, so one can collapse a compressed Laplacian pyramid•Denoising/Restoration Setting “most” coefficients in Laplacian pyramid to zero•Image Blending
Application: Pyramid Blending
0
1
0
1
0
1
Left pyramid Right pyramidblend
Image Blending
Feathering
01
01
+
=Encoding transparency
I(x,y) = (R, G, B, )
Iblend = left Ileft + right Iright
Ileft Iright
leftright
Affect of Window Size
0
1 left
right0
1
Affect of Window Size
0
1
0
1
Good Window Size
0
1
“Optimal” Window: smooth but not ghostedBurt and Adelson (83): Choose by pyramids…
Pyramid Blending
Pyramid Blending (Color)
Laplacian Pyramid: BlendingGeneral Approach:
1. Build Laplacian pyramids LA and LB from images A and B2. Build a Gaussian pyramid GR from selected region R (black
white corresponding images)3. 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
laplacianlevel
4
laplacianlevel
2
laplacianlevel
0
left pyramid right pyramid blended pyramid
Blending Regions
Simplification: Two-band BlendingBrown & Lowe, 2003
• Only use two bands: high freq. and low freq.• Blends low freq. smoothly• Blend high freq. with no smoothing: use binary mask
Can be explored in a project…