Download int always rounds

Image Enhancement
Christoph Lampert / Chris Wojtan
Some slides adapted from Selim Aksoy, Bilkent University
Representing an Image in Code
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2D matrix of values, starting from the upper left
(start counting at zero)
Pixel [1][3]
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Integer, floating point, …, ?
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boolean (0=black, 1=white)
floating point (0.0=black, 1.0=white)
8-bit integer (0=black, 255=white) – most common
Whatever is convenient
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Floating point is good for continuous math
(exponentiation, division, …)
Integers are useful for dividing up values into discrete sets
(histograms, segmentation, …)
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ImageJ
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You will write some code for homework
Accessing and writing in an image
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image.get(10,15);
tells us the pixel value at row 10, column 15
image.set(10, 25, 0);
sets that pixel value to black
Repeating an operation for every pixel
for (int y=0; y<yMax; y++)
for (int x=0; x<xMax; x++)
image.set(x, y, 0);
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Beware – “casting”
between int and double
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Most images need ints in the end, but intermediate computation should
be in doubles.
int always rounds to an integer. This is usually undesired.
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int y = 0.9;
int y = 128/255;
int y = sin(x);
// y=0
// y=0
// y=0
“Cast” between the two types
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(double) will treat a number like a real value
(int) will treat a number like an integer
int x = 100;
int num_pixels = 1024;
double f = (double) x / (double) num_pixels;
int output_value = (int) (f * 255.0);
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ImageJ Demos
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Black.java
Linear.java
SineWave.java
Checker.java
Circle.java
Max.java
Template.java
Compute_Histogram.java
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Image enhancement
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The principal objective of enhancement is to
process an image so that the result is more
suitable than the original for a specific application.
Enhancement can be done in
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Spatial domain,
Frequency domain.
Common reasons for enhancement include
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Improving visual quality,
Improving machine recognition accuracy.
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Image enhancement
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First, we will consider point processing where
enhancement at any point depends only on the
image value at that point.
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Image enhancement
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First, we will consider point processing where
enhancement at any point depends only on the
image value at that point.
For gray level images, we will use a
transformation function of the form
s = T(r)
where “r” is the original pixel value and “s” is the
new value after enhancement.
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Thresholding
if image.get(x,y) < T
image.set(x,y,0);
else
image.set(x,y,255);
Images from http://www.svi.nl/SeedAndThreshold
Image enhancement
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Image enhancement
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Image enhancement
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Image enhancement
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Image enhancement
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Image enhancement
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Contrast stretching:
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Image enhancement
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Histogram processing
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Histogram processing
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Histogram processing
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Histogram processing
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Histogram processing
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Intuitively, we expect that an image whose pixels
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tend to occupy the entire range of possible gray levels,
tend to be distributed uniformly
will have a high contrast and show a great deal of
gray level detail.
It is possible to develop a transformation function
that can achieve this effect using histograms.
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Histogram equalization
http://fourier.eng.hmc.edu/e161/lectures/contrast_transform/node3.html
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Histogram equalization
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Histogram equalization
Adapted from Wikipedia
CS 484, Fall 2012
©2012, Selim Aksoy
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Histogram specification
whatever the heck you want
The math is similar, but your new image has a
histogram close to any p(y),
instead of a uniform distribution.
http://fourier.eng.hmc.edu/e161/lectures/contrast_transform/node3.html
CS 484, Fall 2012
©2012, Selim Aksoy
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Enhancement using logical operations
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Boolean operations
Enhancement using logical operations
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Boolean operations
Enhancement using logical operations
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Boolean operations
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NOT
AND
OR
XOR
INPUT
OUTPUT
A
B
NOT A
A AND B
A OR B
A XOR B
A AND NOT B
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Enhancement using arithmetic operations
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Addition/Subtraction
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Enhancement using arithmetic operations
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Multiplication
Enhancement using arithmetic operations
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Masking, alpha channel = arithmetic operations
Geometric Transformations
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Geometric Transformations
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Geometric Transformations
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Geometric Transformations in Code
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