cs448f: image processing for photography and vision blending and pyramids

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Post on 16-Dec-2015

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  • Slide 1
  • CS448f: Image Processing For Photography and Vision Blending and Pyramids
  • Slide 2
  • Blending Weve aligned our images. What now? Averaging Weighted averaging min/max/median
  • Slide 3
  • Noise reduction by Averaging
  • Slide 4
  • 2 Shots
  • Slide 5
  • 4 Shots
  • Slide 6
  • 8 Shots
  • Slide 7
  • 16 Shots
  • Slide 8
  • Noise Reduction by Averaging Were averaging random variables X and Y Both have variance S 2 Variance of X+Y = 2S 2 Std.Dev. of X+Y = sqrt(2). S Std.Dev of (X+Y)/2 = sqrt(2)/2. S Ie, every time we take twice as many photos, we reduce noise by sqrt(2)
  • Slide 9
  • Noise Reduction by Averaging Average 4 photos: noise gets reduced 2x Average 8 photos: noise gets reduced 3x Average 16 photos: noise gets reduced 4x
  • Slide 10
  • Noise Reduction by Median (demo)
  • Slide 11
  • Median v Average
  • Slide 12
  • Slide 13
  • Can we identify the bad pixels? Theyre unlike their neighbours Instead of averaging, weighted average where weight = similarity to neighbours
  • Slide 14
  • Weighted Average
  • Slide 15
  • Can we identify the bad pixels? Theyre unlike their neighbours Instead of averaging, weighted average where weight = similarity to neighbours Favors blurriness
  • Slide 16
  • Input
  • Slide 17
  • Other uses of Median Removing Transient Occluders (live demo) (Gates demo) (surf demo)
  • Slide 18
  • Panorama Stitching
  • Slide 19
  • Slide 20
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  • Slide 22
  • Slide 23
  • Multiple Exposure Fusion
  • Slide 24
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  • Slide 28
  • Slide 29
  • Slide 30
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  • Slide 34
  • Focus Fusion
  • Slide 35
  • Slide 36
  • Slide 37
  • Slide 38
  • Slide 39
  • Pyramids Weve been breaking images into two terms for a variety of apps Coarse + Fine More generally we can break it into many terms: Very coarse + finer + finer... + finest.
  • Slide 40
  • Pyramids We can do this by blurring more and more:
  • Slide 41
  • Pyramids And then (optionally) taking differences --
  • Slide 42
  • Pyramids The coarse layers can be stored at low res. Gaussian Pyramid Laplacian Pyramid
  • Slide 43
  • Pyramids How much memory does this use?
  • Slide 44
  • Pyramid Uses: Sampling arbitrarily sized Gaussians Equalizing an image The different levels represent different frequency ranges We can scale each frequency level and recombine Blending multiple images
  • Slide 45
  • Pyramid Blending Key Insight: Coarse structure should blend very slowly between images (lots of feathering), while fine details should transition more quickly. More robust to tricky cases than plain old compositing
  • Slide 46
  • Inputs:
  • Slide 47
  • Compositing: Hard Mask
  • Slide 48
  • Compositing: Soft Mask
  • Slide 49
  • Multi-Band Blending
  • Slide 50
  • Exposure Fusion http://research.edm.uhasselt.be/~tmertens/papers/exposure_fusion_reduced.pdf