digital image processing-computer science

1. P R ES EN T ED BY
EM A N T A R EK
Digital Image
Processing
An Introduction to Digital Image Processing
with GNU Octave

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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.
Types of digital image

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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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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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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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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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Image Types

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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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Example 5
Write a Matlab program that read an RGB image then separate it’s
three color channels.

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Example 5 Cont.
Write a Matlab program that read an RGB image then separate it’s
three color channels.

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Example 5 Cont.
Write a Matlab program that read an RGB image then separate it’s
three color channels.

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Example 6
Convert intensity image to index image
gray2ind - intensity image to index image
A=Imread(‘ cameraman.tif’);
[indimg,map]=Gray2ind(A);
Indexed image
Grayscale image

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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
Grayscale image

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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-dimensionalmatrix, all three values
will be the same.

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Iminfo() function

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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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Simultaneous Contrast
There are a number of things to bear in mind:
Observed intensities vary as to the background.

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Example 7

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 Spatial resolution is the density of pixels over the image:
 The greater the spatial resolution, the more pixels are used
to display the image.
 We can experiment with spatial resolution
with imresize function.
Suppose we have an 256*256 8-bit
grayscale image saved to the matrix x.
Then the command:
 will halve the size of the image….
Spatial Resolution

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 B=imresize(A,scale)
 If scale is between 0 and 1 the return is less than the actual size.
 If scale is greater than 1the return is greater than the actual size.
Imresize() Function

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 Greyscale images can be transformed into a sequence of
binary images by breaking them up into their bit-planes.
 We consider the grey value of each pixel of an 8-bit image
as an 8-bit binary word.
 The 0th bit plane consists of the last bit of each grey value.
 Since this bit has the least effect (least significant bit
plane).
 The 7th bit plane consists of the first bit in each value (most
significant bit plane).
Bit Planes

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Bit plane Slicing
Instead of highlighting gray level images, highlight the contribution
made by specific bits s to total image appearance

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Bit plane Slicing
plane 0  contains the lowest order bit of all the pixels in the image.
plane 7  contains the highest order bit of all the pixels in the image.

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Bit plane Slicing

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Bit plane Slicing

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Example 8
Write a Matlab program that generate all bit planes of a gray scale
image

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Example 8 Cont.

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Bit plane Slicing
• Image reconstruction using n bit planes