Intensity Transformations
Examples
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Image and Multidimensional Signal Processing
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Example 1 – Dehazing
• Images of outdoor scenes are often degraded by haze (due to
fog, dust, etc). Light is attenuated by its passage through the
atmosphere, and additional unwanted scattered light is
added.
• We want to enhance the images to remove the effect of the
haze.
Raw
Two aerial
images
with a lot
of haze
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Images are
courtesy of
Microsoft
Bing Maps
group
Image and Multidimensional Signal Processing
2
Example 1 – Dehazing (continued)
• Look at input histograms of images “haze1.tif” and “haze2.tif”.
Iin = imread('haze1.tif');
% Look at input histograms.
I = imresize(Iin, 0.2);
figure, imshow(I), title('Input');
Rx = imhist(I(:,:,1),256);
Gx = imhist(I(:,:,2),256);
Bx = imhist(I(:,:,3),256);
figure, plot(0:255, Rx, 'r', 0:255, Gx, 'g', 0:255, Bx, 'b');
title('Input histograms');
• Let’s assume that the pixels in the degraded image that have
the lowest values (in each color band) actually should be pure
black, and the pixels that have the highest values (in each
color band) are actually should be pure white.
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Image and Multidimensional Signal Processing
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Example 1 – Dehazing (continued)
• A simple de-hazing algorithm is to take a color image, and for each band
(red, green, blue), stretch its values to occupy the full range (0 to 255),
such that 1% of data at the extreme ends is saturated at the low and high
values (see Matlab’s “imadjust”). Then put the bands back together into
an RGB image.
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Image and Multidimensional Signal Processing
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Example 2 – histogram equalization
• Manual histogram equalization
• Read image “Fig0309(a).tif”
• Reduce range of gray levels to 0..15
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Image and Multidimensional Signal Processing
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Example 2 - histogram equalization (continued)
clear all
close all
I = imread('Fig0309(a).tif');
I = I/16;
% Reduce gray levels from 0..255 to 0..15
imshow(I,[]);
[H,X] = imhist(I);
% Get histogram
disp('Initial histogram:');
disp([X(1:16) H(1:16)]);
% Display entries for
0..15
Colorado School of Mines
Image and Multidimensional Signal Processing
Initial histogram:
0
875
1
0
2
0
3
2855
4
7727
5
9021
6
6458
7
8672
8
11475
9
19487
10
39222
11
50467
12
77795
13 142872
14 117743
15
65031
6
Example 2 - histogram equalization (continued)
• Using an Excel spreadsheet and following the examples in
Lecture 5, do the following:
– Compute the transformation function s = T(r) that will equalize the
histogram.
– Estimate the histogram of the output (equalized) image.
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Image and Multidimensional Signal Processing
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