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Transcript of Histograms and Color Balancing Computational Photography Derek Hoiem, University of Illinois...
![Page 1: Histograms and Color Balancing Computational Photography Derek Hoiem, University of Illinois 09/13/11 “Empire of Light”, Magritte.](https://reader036.fdocuments.in/reader036/viewer/2022062423/5697bfbb1a28abf838ca0b37/html5/thumbnails/1.jpg)
Histograms and Color Balancing
Computational PhotographyDerek Hoiem, University of Illinois
09/13/11
“Empire of Light”, Magritte
![Page 2: Histograms and Color Balancing Computational Photography Derek Hoiem, University of Illinois 09/13/11 “Empire of Light”, Magritte.](https://reader036.fdocuments.in/reader036/viewer/2022062423/5697bfbb1a28abf838ca0b37/html5/thumbnails/2.jpg)
Administrative stuff
• New office hours: Monday 10-11, 3-4
• Project 1 in, more discussion next Tues– Vote for class favorite overall project and result;
can vote for multiple projects/results
![Page 3: Histograms and Color Balancing Computational Photography Derek Hoiem, University of Illinois 09/13/11 “Empire of Light”, Magritte.](https://reader036.fdocuments.in/reader036/viewer/2022062423/5697bfbb1a28abf838ca0b37/html5/thumbnails/3.jpg)
Review of last class
Possible factors: albedo, shadows, texture, specularities, curvature, lighting direction
1.
![Page 4: Histograms and Color Balancing Computational Photography Derek Hoiem, University of Illinois 09/13/11 “Empire of Light”, Magritte.](https://reader036.fdocuments.in/reader036/viewer/2022062423/5697bfbb1a28abf838ca0b37/html5/thumbnails/4.jpg)
Review of last class
12
3
4
5
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1. Why is (2) brighter than (1)? Each points to the asphault. 2. Why is (4) darker than (3)? 4 points to the marking. 3. Why is (5) brighter than (3)? Each points to the side of the wooden block. 4. Why isn’t (6) black, given that there is no direct path from it to the sun?
![Page 5: Histograms and Color Balancing Computational Photography Derek Hoiem, University of Illinois 09/13/11 “Empire of Light”, Magritte.](https://reader036.fdocuments.in/reader036/viewer/2022062423/5697bfbb1a28abf838ca0b37/html5/thumbnails/5.jpg)
Today’s class• How can we represent color?• How do we adjust the intensity of an image to
improve contrast, aesthetics?
![Page 6: Histograms and Color Balancing Computational Photography Derek Hoiem, University of Illinois 09/13/11 “Empire of Light”, Magritte.](https://reader036.fdocuments.in/reader036/viewer/2022062423/5697bfbb1a28abf838ca0b37/html5/thumbnails/6.jpg)
© Stephen E. Palmer, 2002
.
400 450 500 550 600 650
RE
LATI
VE
AB
SO
RB
AN
CE
(%)
WAVELENGTH (nm.)
100
50
440
S
530 560 nm.
M L
Three kinds of cones:
Physiology of Color Vision
• Why are M and L cones so close?• Why are there 3?
![Page 7: Histograms and Color Balancing Computational Photography Derek Hoiem, University of Illinois 09/13/11 “Empire of Light”, Magritte.](https://reader036.fdocuments.in/reader036/viewer/2022062423/5697bfbb1a28abf838ca0b37/html5/thumbnails/7.jpg)
Color spaces: RGB
0,1,0
0,0,1
1,0,0
Image from: http://en.wikipedia.org/wiki/File:RGB_color_solid_cube.png
Some drawbacks• Strongly correlated channels• Non-perceptual
Default color space
R(G=0,B=0)
G(R=0,B=0)
B(R=0,G=0)
![Page 8: Histograms and Color Balancing Computational Photography Derek Hoiem, University of Illinois 09/13/11 “Empire of Light”, Magritte.](https://reader036.fdocuments.in/reader036/viewer/2022062423/5697bfbb1a28abf838ca0b37/html5/thumbnails/8.jpg)
Trichromacy and CIE-XYZ
Perceptual equivalents with RGB Perceptual equivalents with CIE-XYZ
![Page 9: Histograms and Color Balancing Computational Photography Derek Hoiem, University of Illinois 09/13/11 “Empire of Light”, Magritte.](https://reader036.fdocuments.in/reader036/viewer/2022062423/5697bfbb1a28abf838ca0b37/html5/thumbnails/9.jpg)
Color Space: CIE-XYZ
RGB portion is in triangle
![Page 10: Histograms and Color Balancing Computational Photography Derek Hoiem, University of Illinois 09/13/11 “Empire of Light”, Magritte.](https://reader036.fdocuments.in/reader036/viewer/2022062423/5697bfbb1a28abf838ca0b37/html5/thumbnails/10.jpg)
Perceptual uniformity
![Page 11: Histograms and Color Balancing Computational Photography Derek Hoiem, University of Illinois 09/13/11 “Empire of Light”, Magritte.](https://reader036.fdocuments.in/reader036/viewer/2022062423/5697bfbb1a28abf838ca0b37/html5/thumbnails/11.jpg)
Color spaces: CIE L*a*b*“Perceptually uniform” color space
L(a=0,b=0)
a(L=65,b=0)
b(L=65,a=0)
Luminance = brightnessChrominance = color
![Page 12: Histograms and Color Balancing Computational Photography Derek Hoiem, University of Illinois 09/13/11 “Empire of Light”, Magritte.](https://reader036.fdocuments.in/reader036/viewer/2022062423/5697bfbb1a28abf838ca0b37/html5/thumbnails/12.jpg)
If you had to choose, would you rather go without luminance or chrominance?
![Page 13: Histograms and Color Balancing Computational Photography Derek Hoiem, University of Illinois 09/13/11 “Empire of Light”, Magritte.](https://reader036.fdocuments.in/reader036/viewer/2022062423/5697bfbb1a28abf838ca0b37/html5/thumbnails/13.jpg)
If you had to choose, would you rather go without luminance or chrominance?
![Page 14: Histograms and Color Balancing Computational Photography Derek Hoiem, University of Illinois 09/13/11 “Empire of Light”, Magritte.](https://reader036.fdocuments.in/reader036/viewer/2022062423/5697bfbb1a28abf838ca0b37/html5/thumbnails/14.jpg)
Most information in intensity
Only color shown – constant intensity
![Page 15: Histograms and Color Balancing Computational Photography Derek Hoiem, University of Illinois 09/13/11 “Empire of Light”, Magritte.](https://reader036.fdocuments.in/reader036/viewer/2022062423/5697bfbb1a28abf838ca0b37/html5/thumbnails/15.jpg)
Most information in intensity
Only intensity shown – constant color
![Page 16: Histograms and Color Balancing Computational Photography Derek Hoiem, University of Illinois 09/13/11 “Empire of Light”, Magritte.](https://reader036.fdocuments.in/reader036/viewer/2022062423/5697bfbb1a28abf838ca0b37/html5/thumbnails/16.jpg)
Most information in intensity
Original image
![Page 17: Histograms and Color Balancing Computational Photography Derek Hoiem, University of Illinois 09/13/11 “Empire of Light”, Magritte.](https://reader036.fdocuments.in/reader036/viewer/2022062423/5697bfbb1a28abf838ca0b37/html5/thumbnails/17.jpg)
Color spaces: HSV
Intuitive color space
H(S=1,V=1)
S(H=1,V=1)
V(H=1,S=0)
![Page 18: Histograms and Color Balancing Computational Photography Derek Hoiem, University of Illinois 09/13/11 “Empire of Light”, Magritte.](https://reader036.fdocuments.in/reader036/viewer/2022062423/5697bfbb1a28abf838ca0b37/html5/thumbnails/18.jpg)
Color spaces: YCbCr
Y(Cb=0.5,Cr=0.5)
Cb(Y=0.5,Cr=0.5)
Cr(Y=0.5,Cb=05)
Y=0 Y=0.5
Y=1Cb
Cr
Fast to compute, good for compression, used by TV
![Page 19: Histograms and Color Balancing Computational Photography Derek Hoiem, University of Illinois 09/13/11 “Empire of Light”, Magritte.](https://reader036.fdocuments.in/reader036/viewer/2022062423/5697bfbb1a28abf838ca0b37/html5/thumbnails/19.jpg)
Color balancing
http://en.wikipedia.org/wiki/Histogram_equalization
![Page 20: Histograms and Color Balancing Computational Photography Derek Hoiem, University of Illinois 09/13/11 “Empire of Light”, Magritte.](https://reader036.fdocuments.in/reader036/viewer/2022062423/5697bfbb1a28abf838ca0b37/html5/thumbnails/20.jpg)
Color balancing
Photos: http://www.kenrockwell.com/tech/whitebalance.htm
![Page 21: Histograms and Color Balancing Computational Photography Derek Hoiem, University of Illinois 09/13/11 “Empire of Light”, Magritte.](https://reader036.fdocuments.in/reader036/viewer/2022062423/5697bfbb1a28abf838ca0b37/html5/thumbnails/21.jpg)
Important ideas
• Typical images are gray on average; this can be used to detect distortions
• Larger differences are more visible, so using the full intensity range improves visibility
• It’s often easier to work in a non-RGB color space
![Page 22: Histograms and Color Balancing Computational Photography Derek Hoiem, University of Illinois 09/13/11 “Empire of Light”, Magritte.](https://reader036.fdocuments.in/reader036/viewer/2022062423/5697bfbb1a28abf838ca0b37/html5/thumbnails/22.jpg)
Color balancing via linear adjustment• Simple idea: multiply R, G, and B values by
separate constants=
• How to choose the constants?– “Gray world” assumption: average value should be
gray– White balancing: choose a reference as the white or
gray color– Better to balance in camera’s RGB (linear) than
display RGB (non-linear)
![Page 23: Histograms and Color Balancing Computational Photography Derek Hoiem, University of Illinois 09/13/11 “Empire of Light”, Magritte.](https://reader036.fdocuments.in/reader036/viewer/2022062423/5697bfbb1a28abf838ca0b37/html5/thumbnails/23.jpg)
Tone Mapping• Typical problem: compress values from a high
range to a smaller range– E.g., camera captures 12-bit linear intensity and
needs to compress to 8 bits
![Page 24: Histograms and Color Balancing Computational Photography Derek Hoiem, University of Illinois 09/13/11 “Empire of Light”, Magritte.](https://reader036.fdocuments.in/reader036/viewer/2022062423/5697bfbb1a28abf838ca0b37/html5/thumbnails/24.jpg)
Example: Linear display of HDR
Scaled for brightest pixels Scaled for darkest pixels
![Page 25: Histograms and Color Balancing Computational Photography Derek Hoiem, University of Illinois 09/13/11 “Empire of Light”, Magritte.](https://reader036.fdocuments.in/reader036/viewer/2022062423/5697bfbb1a28abf838ca0b37/html5/thumbnails/25.jpg)
Global operator (Reinhart et al.)• Simple solution: map to a non-linear range of
values
world
worlddisplay L
LL
1
![Page 26: Histograms and Color Balancing Computational Photography Derek Hoiem, University of Illinois 09/13/11 “Empire of Light”, Magritte.](https://reader036.fdocuments.in/reader036/viewer/2022062423/5697bfbb1a28abf838ca0b37/html5/thumbnails/26.jpg)
Darkest 0.1% scaledto display device
Reinhart Operator
![Page 27: Histograms and Color Balancing Computational Photography Derek Hoiem, University of Illinois 09/13/11 “Empire of Light”, Magritte.](https://reader036.fdocuments.in/reader036/viewer/2022062423/5697bfbb1a28abf838ca0b37/html5/thumbnails/27.jpg)
Simple Point Processing
Some figs from A. Efros’ slides
![Page 28: Histograms and Color Balancing Computational Photography Derek Hoiem, University of Illinois 09/13/11 “Empire of Light”, Magritte.](https://reader036.fdocuments.in/reader036/viewer/2022062423/5697bfbb1a28abf838ca0b37/html5/thumbnails/28.jpg)
Negative
![Page 29: Histograms and Color Balancing Computational Photography Derek Hoiem, University of Illinois 09/13/11 “Empire of Light”, Magritte.](https://reader036.fdocuments.in/reader036/viewer/2022062423/5697bfbb1a28abf838ca0b37/html5/thumbnails/29.jpg)
Log
![Page 30: Histograms and Color Balancing Computational Photography Derek Hoiem, University of Illinois 09/13/11 “Empire of Light”, Magritte.](https://reader036.fdocuments.in/reader036/viewer/2022062423/5697bfbb1a28abf838ca0b37/html5/thumbnails/30.jpg)
Power-law transformations
𝑠=𝑟 𝛾
![Page 31: Histograms and Color Balancing Computational Photography Derek Hoiem, University of Illinois 09/13/11 “Empire of Light”, Magritte.](https://reader036.fdocuments.in/reader036/viewer/2022062423/5697bfbb1a28abf838ca0b37/html5/thumbnails/31.jpg)
Image Enhancement
![Page 32: Histograms and Color Balancing Computational Photography Derek Hoiem, University of Illinois 09/13/11 “Empire of Light”, Magritte.](https://reader036.fdocuments.in/reader036/viewer/2022062423/5697bfbb1a28abf838ca0b37/html5/thumbnails/32.jpg)
Contrast Stretching
![Page 33: Histograms and Color Balancing Computational Photography Derek Hoiem, University of Illinois 09/13/11 “Empire of Light”, Magritte.](https://reader036.fdocuments.in/reader036/viewer/2022062423/5697bfbb1a28abf838ca0b37/html5/thumbnails/33.jpg)
Histogram equalization• Basic idea: reassign values so that the number
of pixels with each value is more evenly distributed
• Histogram: a count of how many pixels have each value
• Cumulative histogram: count of number of pixels less than or equal to each value
![Page 34: Histograms and Color Balancing Computational Photography Derek Hoiem, University of Illinois 09/13/11 “Empire of Light”, Magritte.](https://reader036.fdocuments.in/reader036/viewer/2022062423/5697bfbb1a28abf838ca0b37/html5/thumbnails/34.jpg)
Image Histograms
Cumulative Histograms
![Page 35: Histograms and Color Balancing Computational Photography Derek Hoiem, University of Illinois 09/13/11 “Empire of Light”, Magritte.](https://reader036.fdocuments.in/reader036/viewer/2022062423/5697bfbb1a28abf838ca0b37/html5/thumbnails/35.jpg)
Histogram Equalization
![Page 36: Histograms and Color Balancing Computational Photography Derek Hoiem, University of Illinois 09/13/11 “Empire of Light”, Magritte.](https://reader036.fdocuments.in/reader036/viewer/2022062423/5697bfbb1a28abf838ca0b37/html5/thumbnails/36.jpg)
Algorithm for global histogram equalization
Goal: Given image with pixel valuesspecify function that remaps pixel values, so that the new values are more broadly distributed
1. Compute cumulative histogram: ,
2. – Blends between original image and image with
uniform histogram
![Page 37: Histograms and Color Balancing Computational Photography Derek Hoiem, University of Illinois 09/13/11 “Empire of Light”, Magritte.](https://reader036.fdocuments.in/reader036/viewer/2022062423/5697bfbb1a28abf838ca0b37/html5/thumbnails/37.jpg)
Locally weighted histograms• Compute cumulative histograms in non-
overlapping MxM grid• For each pixel, interpolate between the
histograms from the four nearest grid cells
Figure from Szeliski book (Fig. 3.9)Pixel (black) is mapped based on interpolated value from its cell and nearest horizontal, vertical, diagonal neighbors
![Page 38: Histograms and Color Balancing Computational Photography Derek Hoiem, University of Illinois 09/13/11 “Empire of Light”, Magritte.](https://reader036.fdocuments.in/reader036/viewer/2022062423/5697bfbb1a28abf838ca0b37/html5/thumbnails/38.jpg)
Other issues• Dealing with color images
– Often better to split into luminance and chrominance to avoid unwanted color shift
• Manipulating particular regions– Can use mask to select particular areas for
manipulation
• Useful Matlab functions– rgb2hsv, hsv2rgb, hist, cumsum,
![Page 39: Histograms and Color Balancing Computational Photography Derek Hoiem, University of Illinois 09/13/11 “Empire of Light”, Magritte.](https://reader036.fdocuments.in/reader036/viewer/2022062423/5697bfbb1a28abf838ca0b37/html5/thumbnails/39.jpg)
Project 2: due Sept 26• Part I: Alignment• Part II: Enhance contrast and color
![Page 40: Histograms and Color Balancing Computational Photography Derek Hoiem, University of Illinois 09/13/11 “Empire of Light”, Magritte.](https://reader036.fdocuments.in/reader036/viewer/2022062423/5697bfbb1a28abf838ca0b37/html5/thumbnails/40.jpg)
Examples with Matlab
![Page 41: Histograms and Color Balancing Computational Photography Derek Hoiem, University of Illinois 09/13/11 “Empire of Light”, Magritte.](https://reader036.fdocuments.in/reader036/viewer/2022062423/5697bfbb1a28abf838ca0b37/html5/thumbnails/41.jpg)
Things to remember
• Familiarize yourself with the basic color spaces: RGB, HSV, Lab
• Simple auto contrast/color adjustments: gray world assumption, histogram equalization
• When improving contrast in a color image, often best to operate on luminance channel
![Page 42: Histograms and Color Balancing Computational Photography Derek Hoiem, University of Illinois 09/13/11 “Empire of Light”, Magritte.](https://reader036.fdocuments.in/reader036/viewer/2022062423/5697bfbb1a28abf838ca0b37/html5/thumbnails/42.jpg)
Next class: finding seams