digital image processing-computer science

1. Digital Image
Processing
An Introduction to Digital Image Processing
with GNU Octave

2. 1. Binary: Each pixel is just black or white. Since there are only two possible
values for each pixel (0,1), we only need one bit per pixel.
2. Grayscale: Each pixel is a shade of gray, normally from 0 (black) to 255
(white). This range means that each pixel can be represented by eight bits,
or exactly one byte. Other greyscale ranges are used, but generally they are
a power of 2.
3. True Color, or RGB: Each pixel has a particular color; that color is described
by the amount of red, green and blue in it. If each of these components has
a range 0–255, this gives a total of 2563 different possible colors. Such an
image is a “stack” of three matrices; representing the red, green and blue
values for each pixel. This means that for every pixel there correspond 3
values.
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Types of digital image

3. 1. Binary Image
• Binary: two possible values for each pixel, we only need one bit per
pixel.
• Each pixel is just black or white.
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4. 2. Grayscale Image
⚫ Grayscale: Each pixel is a shade of grey, normally from .
⚫ means that each pixel can be represented by eight bits, or exactly one
byte.
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5. 3. Color Image
⚫ True color or RGB: here each pixel has a particular color, that color
being described by the amount of red, green and blue in it.
⚫ Such an image may be considered as consisting of a stack of three
matrices; representing the red, green and blue values for each pixel.
⚫ This means that for every pixel there correspond three values.
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6. 4. Indexed Image
⚫ Indexed: Most color images only have a small subset of the more than
sixteen million possible colors.
⚫ For convenience of storage and le handling, the image has an
associated color map.
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7. Image Types
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8. Example 4
Given the image ’’Lion.jpg’’, write a Matlab program to generate all
different image types and display all of them.
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9. Example 5
Write a Matlab program that read an RGB image then separate it’s
three color channels.
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10. Example 5 Cont.
Write a Matlab program that read an RGB image then separate it’s
three color channels.
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11. Example 5 Cont.
Write a Matlab program that read an RGB image then separate it’s
three color channels.
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12. Example 6
Convert intensity image to index image
gray2ind - intensity image to index image
Indexed image
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A=Imread(‘ cameraman.tif’);
[indimg,map]=Gray2ind(A);
Grayscale image

13. Example 6 Cont.
Convert index image to intensity image
ind2gray - indexed image to intensity image
Code
Load trees
Imshow(X,map)
Grayimg=Ind2gray(X,map);
Imshow(Grayimg)
Indexed image
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Grayscale image

14. Impixel(i,j) function
⚫ A useful function for obtaining RGB values is impixel
returns the red, green, and blue values of the
pixel at column 200, row 100.
⚫ This command also applies to grayscale images:
return three values, but since g is a single
two-dimensional matrix, all three values
will be the same.
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15. Iminfo() function
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16. Iminfo() function
we can see :
 the size of the image in pixels,
 the size of the file (in bytes),
 the number of bits per pixel (this is given by BitDepth),
 and the color type (in this case indexed).
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17. Simultaneous Contrast
There are a number of things to bear in mind:
Observed intensities vary as to the background.
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18. Example 7
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