Introduction to Image Processing and
Python: Brightness and Contrast
Brightness and Contrast
Steven L. Tanimoto
Intro. to Image Proc. & Python © S. Tanimoto
Saturday, August 24, 13
Brightness and Contrast
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Outline
Brightness
Contrast
Pixel Arithmetic and Truncation
Histograms
Thresholding
Intro. to Image Proc. & Python © S. Tanimoto
Saturday, August 24, 13
Brightness and Contrast
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Brightness
Brightness is the relative light intensity of some part of the
image, typically of a single pixel.
Some people make a distinction between actual physical light
intensity (as measured by a light meter) and the perception of
intensity, which can be more subjective.
Whereas light intensity may be measured in physical units such
as “foot candles”, lumens, etc., brightness in a digital image is
simply in “brightness units.” The actual light intensity of a pixel
of a digital image depends on factors such as what kind of
computer monitor is displaying the image, whether the intensity
control is turned all the way up, or in the case of a printout,
how much illumination there is on the picture.
Intro. to Image Proc. & Python © S. Tanimoto
Saturday, August 24, 13
Brightness and Contrast
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Brightness (continued)
Brightness for a color pixel may be considered to be the
average value of the color components: (R + G + B)/3.
Thus brightness in PixelMath ranges from 0 to 255, just
as the color components do.
Intro. to Image Proc. & Python © S. Tanimoto
Saturday, August 24, 13
Brightness and Contrast
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Adjusting Brightness
Here are two simple ways to adjust the brightness of an
image:
1. Add a number to every pixel value in the image.
P is replaced by P + d, where d represents the
difference.
If d > 0, then the image will generally get brighter.
(We say “generally,” because if the image is already as
bright as possible, then it can’t get any brighter.)
If d < 0, then the image will generally get darker.
2.
Multiply every pixel value by a factor.
P is replaced by P * f, where f is the factor.
If f > 1, then the image will generally get brighter.
If f < 1, then the image will generally get darker.
Intro. to Image Proc. & Python © S. Tanimoto
Saturday, August 24, 13
Brightness and Contrast
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Adjusting Brightness (cont.)
Multiplying also generally changes the contrast of the
image.
If f > 1, then the pixel values typically end up more
widely spread (but not always, due to the truncation
property of “Pixel Arithmetic”).
If f < 1, then the pixel values end up less widely spread.
Intro. to Image Proc. & Python © S. Tanimoto
Saturday, August 24, 13
Brightness and Contrast
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Pixel Arithmetic
The PixelMath system computes values with great accuracy,
using “double precision” computer representations of each
number.
However, when the numbers are ready to be put into the
destination image’s pixels, they may be changed.
First, they are “truncated.” This means that any negative
number is changed to 0.0. Also, any number greater than
255.0 is changed to 255.0.
Next, they are converted to integers. This means that a
number such as 37.886 gets changed to 37. Any number
that is equal to an integer (201.00) is not changed in value
but is changed in form to an integer (201).
The name “floor” is given to the function that gives back the
integer, but we don’t have to ask for it; it’s done
automatically in PixelMath.
Intro. to Image Proc. & Python © S. Tanimoto
Saturday, August 24, 13
Brightness and Contrast
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Pixel Arithmetic (continued)
In the PixelMath system, what will be the result (in the
destination image), for the following operations?
1+1
255 + 1
1.5 + 1.6
100 * 3.5
9 / 10
100.0 * - 2.5
Intro. to Image Proc. & Python © S. Tanimoto
Saturday, August 24, 13
Brightness and Contrast
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Coordinating Brightness and
Contrast
To control both brightness and contrast it is typical to
multiply and add at the same time.
Replace p by (f * p) + d.
For example, with f = 2.0 and d = -100, we have
pnew = (2.0 * pold) - 100
This this would theoretically be a linear function, (and its
graph would be a line, with slope f, and intercept d),
but PixelMath pixels cannot be less than 0 or greater
than 255, so the actual graph is a piecewise linear
function…
Intro. to Image Proc. & Python © S. Tanimoto
Saturday, August 24, 13
Brightness and Contrast
9
Coordinating Brightness and
Contrast (cont.)
pnew = (2.0 * pold) - 100
255
pnew
0
0
Intro. to Image Proc. & Python © S. Tanimoto
Saturday, August 24, 13
pold
255
Brightness and Contrast
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Histograms
A histogram is a graph showing the frequency of occurrence of
each of a set of values.
Here is a non-image-processing example:
Musical note sequence*:
Histogram:
A
B
“EEFGGFEDCCDEEDD”
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
C
D
E
F
G
* Theme from Beethoven’s 9th symphony.
Intro. to Image Proc. & Python © S. Tanimoto
Saturday, August 24, 13
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Histogram “Bins”
Each column of a histogram is called a bin.
A
B
X
X
X
X
X
X
X
X
X
X
X
X
X
X
X
C
D
E
F
G
Intro. to Image Proc. & Python © S. Tanimoto
Saturday, August 24, 13
Brightness and Contrast
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Pixel Value Histograms
A pixel value histogram is a graph showing the frequency of
occurrence of each of the possible pixel values in an image.
Intro. to Image Proc. & Python © S. Tanimoto
Saturday, August 24, 13
Brightness and Contrast
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Histogram Equalization
To “equalize” a histogram, we change the original item values
in such a way that the new histogram will have a more even
distribution of values. (In the following example all Cs will
become As, and some other notes will be changed, too.)
For this to work, there must be some spread already – if all the
values are in one bin, they will stay in one bin.
Original histogram:
A
B
X
X
C
X
X
X
X
D
X
X
X
X
X
E
Let’s use numbers
instead of X’s
X
X
F
X
X
G
Intro. to Image Proc. & Python © S. Tanimoto
Saturday, August 24, 13
0
A
0
B
2
C
4
D
5
E
2
F
2
G
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Histogram Equalization (cont.)
Ideal cumulative histogram:
Original histogram:
0
A
0
B
2
C
4
D
5
E
2
F
2
G
Cumulative
histogram:
0 0 2 6 11 13 15
A B C D E F G
There should be about 2
items per bin, on average.
Intro. to Image Proc. & Python © S. Tanimoto
Saturday, August 24, 13
2
A
4
B
6
C
8 10 12 15
D E F G
Equalized histogram:
2
A
0
B
4
C
0
D
5
E
2
F
2
G
Cumulative equalized
histogram:
2 2 6 6 11 13 15
A B C D E F G
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Purpose of Equalization
The main reason to perform histogram equalization is to
better spread out the pixels values within the available range
(0 to 255), so that the differences in brightness among the
pixels can be more readily seen, or so that automatic
algorithms can take advantage of greater differences among
the pixel values.
Equalization can be used for contrast enhancement. It tends
to accentuate differences in brightness without throwing away
any of those differences. As we’ll see, thresholding does a
great job of increasing contrast, but at a cost: although some
differences are amplified, many of the differences among
pixel values are lost.
Intro. to Image Proc. & Python © S. Tanimoto
Saturday, August 24, 13
Brightness and Contrast
16
Making a (Gray) Monochrome
PixelMath formula:
(Red1(x,y)+Green1(x,y)+Blue1(x,y))/3
This is an average of the R, G, and B
components.
In the monochrome image, each of the
color components will be given this
average value.
After doing this, the red, green, and
blue values within a pixel will be equal.
Then the histograms for red, green
and blue will be identical, and any one
of them can serve to represent the
others.
Intro. to Image Proc. & Python © S. Tanimoto
Saturday, August 24, 13
Brightness and Contrast
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“Equalized Mono Mona”
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“Equalized Histogram for Mona”
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Thresholding
A “threshold” is a value used to separate “low values” from “high
values”. What would be a good threshold?
Intro. to Image Proc. & Python © S. Tanimoto
Saturday, August 24, 13
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Thresholding
A “threshold” is a value used to separate “low values” from “high
values”. Often a “valley” leads to a good threshold.
p
Valley at p = 113
Intro. to Image Proc. & Python © S. Tanimoto
Saturday, August 24, 13
Brightness and Contrast
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Thresholding: PixelMath Formula
Condition
then value
else value
if Source1(x,y) < 113 then 0 else 255
Source image pixel
comparison
Intro. to Image Proc. & Python © S. Tanimoto
Saturday, August 24, 13
threshold
black
Brightness and Contrast
white
22
Thresholding
Resulting image has a histogram with only two non-empty bins (0 and
255).
# black pixels (p=0)
is now 81384
# white pixels (p = 255) is now 96699
Total number of pixels is (always) 178083.
Intro. to Image Proc. & Python © S. Tanimoto
Saturday, August 24, 13
Brightness and Contrast
23
Thresholding (Result)
Intro. to Image Proc. & Python © S. Tanimoto
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Brightness and Contrast
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Comparing Thresholds
Threshold = 175
Threshold = 113
Intro. to Image Proc. & Python © S. Tanimoto
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Threshold = 90
Brightness and Contrast
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