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CS4670: Computer Vision
CS4670: Computer Vision
CS4670: Computer Vision
CS4670: Computer Vision
CS4670: Computer Vision
CS4670: Computer Vision
CS4670: Computer Vision
CS4670: Computer Vision
Photometric stereo
Photometric stereo
Finding the direction of the light source
Finding the direction of the light source
Application: Detecting composite photos
Application: Detecting composite photos
Application: Detecting composite photos
Application: Detecting composite photos
Application: Detecting composite photos
Application: Detecting composite photos
Application: Detecting composite photos
Application: Detecting composite photos
The ultimate camera
The ultimate camera
Noise reduction
Noise reduction
Noise reduction
Noise reduction
Noise reduction
Noise reduction
Noise reduction
Noise reduction
Noise reduction
Noise reduction
Noise reduction
Noise reduction
Field of view
Field of view
Improving resolution: Gigapixel images
Improving resolution: Gigapixel images
Intuition (slides from Yossi Rubner & Miki Elad)
Intuition (slides from Yossi Rubner & Miki Elad)
Example
Example
Example
Example
Example
Example
Example
Example
Example
Example
Example
Example
Example
Example
Example
Example
Example
Example
Example
Example
Example
Example
Example
Example
Example
Example
Example
Example
Example
Example
Dynamic Range
Dynamic Range
Dynamic Range
Dynamic Range
Dynamic Range
Dynamic Range
HDR images — merge multiple inputs
HDR images — merge multiple inputs
HDR images — merge multiple inputs
HDR images — merge multiple inputs
HDR images — merge multiple inputs
HDR images — merge multiple inputs
HDR images — merged
HDR images — merged
Camera response function
Camera response function
Light field camera [Ng et al
Light field camera [Ng et al
Light field camera [Ng et al
Light field camera [Ng et al
Conventional vs
Conventional vs
Conventional vs
Conventional vs
Light field camera
Light field camera
Prototype camera
Prototype camera
Prototype camera
Prototype camera
Prototype camera
Prototype camera
Prototype camera
Prototype camera
Prototype camera
Prototype camera
CS4670: Computer Vision
CS4670: Computer Vision
CS4670: Computer Vision
CS4670: Computer Vision
What can we do with the captured rays
What can we do with the captured rays
CS4670: Computer Vision
CS4670: Computer Vision
CS4670: Computer Vision
CS4670: Computer Vision
CS4670: Computer Vision
CS4670: Computer Vision
Example of digital refocusing
Example of digital refocusing
Example of digital refocusing
Example of digital refocusing
Example of digital refocusing
Example of digital refocusing
Example of digital refocusing
Example of digital refocusing
Example of digital refocusing
Example of digital refocusing
All-in-focus images
All-in-focus images
Why are images blurry
Why are images blurry
Why are images blurry
Why are images blurry
Why are images blurry
Why are images blurry
Motion blur
Motion blur
Motion blur
Motion blur
Motion blur
Motion blur
=
=
=
=
=
=
Priors can help
Priors can help
Priors can help
Priors can help
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CS4670: Computer Vision

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1CS4670: Computer Vision. Noah Snavely. 17down and to the right we get the fourth
Lecture 33: Computational photography. image: 2D. 2D. 17.
2Photometric stereo. 18Intuition. By combining all four
3Limitations. Big problems doesn’t work images the desired resolution is obtained,
for shiny things, semi-translucent things and thus perfect reconstruction is
shadows, inter-reflections Smaller guaranteed. 18.
problems camera and lights have to be 19Example. 3:1 scale-up in each axis
distant calibration requirements measure using 9 images, with pure global
light source directions, intensities translation between them. 19.
camera response function Newer work 20Dynamic Range. Typical cameras have
addresses some of these issues Some limited dynamic range.
pointers for further reading: Zickler, 21HDR images — merge multiple inputs.
Belhumeur, and Kriegman, "Helmholtz 22HDR images — merged. Pixel count.
Stereopsis: Exploiting Reciprocity for Radiance.
Surface Reconstruction." IJCV, Vol. 23Camera is not a photometer! Limited
49 No. 2/3, pp 215-227. Hertzmann & dynamic range 8 bits captures only 2
Seitz, “Example-Based Photometric Stereo: orders of magnitude of light intensity We
Shape Reconstruction with General, Varying can see ~10 orders of magnitude of light
BRDFs.” IEEE Trans. PAMI 2005. intensity Unknown, nonlinear response
4Finding the direction of the light pixel intensity ? amount of light (#
source. P. Nillius and J.-O. Eklundh, photons, or “radiance”) Solution: Recover
“Automatic estimation of the projected response curve from multiple exposures,
light source direction,” CVPR 2001. then reconstruct the radiance map.
5Application: Detecting composite 24Camera response function.
photos. Which is the real photo? Fake 25Capture and composite several photos.
photo. Real photo. Works for field of view resolution signal
6The ultimate camera. What does it do? to noise dynamic range Focus But sometimes
7The ultimate camera. Infinite you can do better by modifying the camera…
resolution Infinite zoom control Desired 26Focus. Suppose we want to produce
object(s) are in focus No noise No motion images where the desired object is
blur Infinite dynamic range (can see dark guaranteed to be in focus? Or suppose we
and bright things) ... want everything to be in focus?
8Creating the ultimate camera. The 27Light field camera [Ng et al., 2005].
“analog” camera has changed very little in http://www.refocusimaging.com/gallery/.
>100 yrs we’re unlikely to get there 28Conventional vs. light field camera.
following this path More promising is to Conventional camera. Light field camera.
combine “analog” optics with computational 29Light field camera. Rays are
techniques “Computational cameras” or reorganized into many smaller images
“Computational photography” This lecture corresponding to subapertures of the main
will survey techniques for producing lens.
higher quality images by combining optics 30Prototype camera. Contax medium format
and computation Common themes: take camera. Kodak 16-megapixel sensor. 4000 ?
multiple photos modify the camera. 4000 pixels ? 292 ? 292 lenses = 14 ? 14
9Noise reduction. Take several images pixels per lens.
and average them Why does this work? Basic 31
statistics: variance of the mean decreases 32What can we do with the captured rays?
with n: Change viewpoint.
10Field of view. We can artificially 33
increase the field of view by compositing 34Example of digital refocusing.
several photos together (project 2). 35All-in-focus images. Combines sharpest
11Improving resolution: Gigapixel parts of all of the individual refocused
images. A few other notable examples: images. Using single pixel from each
Obama inauguration (gigapan.org) HDView subimage.
(Microsoft Research). Max Lyons, 2003 36All-in-focus. If you only want to
fused 196 telephoto shots. produce an all-focus image, there are
12Improving resolution: super simpler alternatives E.g., Wavefront
resolution. What if you don’t have a zoom coding [Dowsky 1995] Coded aperture [Levin
lens? SIGGRAPH 2007], [Raskar SIGGRAPH 2007] can
13Intuition (slides from Yossi Rubner also produce change in focus (ala Ng’s
& Miki Elad). 13. light field camera).
14Intuition (slides from Yossi Rubner 37Why are images blurry? How can we
& Miki Elad). Due to our limited remove the blur? Motion blur.
camera resolution, we sample using an 38Motion blur. Especially difficult to
insufficient 2D grid. 14. remove, because the blur kernel is
15Intuition (slides from Yossi Rubner unknown. both unknown.
& Miki Elad). However, if we take a 39=. ? Multiple possible solutions.
second picture, shifting the camera Sharp image. Blur kernel. Blurry image.
‘slightly to the right’ we obtain: 15. Slide courtesy Rob Fergus.
16Intuition (slides from Yossi Rubner 40Priors can help. Priors on natural
& Miki Elad). Similarly, by shifting images.
down we get a third image: 2D. 2D. 16. 41Natural image statistics.
17Intuition (slides from Yossi Rubner Characteristic distribution with heavy
& Miki Elad). And finally, by shifting tails. Histogram of image gradients.
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