Basic Introduction of MAT Lab in Image Processing
A digital image is composed of pixels
which can be thought of as small dots on the screen. A digital image is an
instruction of how to color each pixel. We will see in detail later on how this
is done in practice. A typical size of an image is 512-by-512 pixels. Later on
in the course you will see that it is convenient to let the dimensions of the
image to be a power of 2. For example, 29=512. In the general case
we say that an image is of size m-by-n if it is composed of m
pixels in the vertical direction and n pixels in the horizontal
direction.
Let us say that we have an image on
the format 512-by-1024 pixels. This means that the data for the image must
contain information about 524288 pixels, which requires a lot of memory! Hence,
compressing images is essential for efficient image processing. You will
later on see how Fourier analysis and Wavelet analysis can help us to compress
an image significantly. There are also a few "computer scientific"
tricks (for example entropy coding) to reduce the amount of data required to
store an image.
Image
formats supported by Matlab
The following image formats are
supported by Matlab:
- BMP
- HDF
- JPEG
- PCX
- TIFF
- XWB
Most images you find on the Internet
are JPEG-images which is the name for one of the most widely used compression
standards for images. If you have stored an image you can usually see from the
suffix what format it is stored in. For example, an image named myimage.jpg is stored in the JPEG format and we will see later on that
we can load an image of this format into Matlab.
Working formats in Matlab
If an image is stored as a JPEG-image on your disc we first
read it into Matlab. However, in order to start working with an image, for
example perform a wavelet transform on the image, we must convert it into a
different format. This section explains four common formats.
Intensity image (gray scale image)
This is the equivalent to a "gray scale image" and
this is the image we will mostly work with in this course. It represents an
image as a matrix where every element has a value corresponding to how
bright/dark the pixel at the corresponding position should be colored. There
are two ways to represent the number that represents the brightness of the
pixel: The double class (or data
type). This assigns a floating number ("a number with decimals")
between 0 and 1 to each pixel. The value 0 corresponds to black and the value 1
corresponds to white. The other class is called uint8 which assigns an integer between 0 and 255 to
represent the brightness of a pixel. The value 0 corresponds to black and 255
to white. The class uint8
only requires roughly 1/8 of the storage compared to the class double. On the other hand, many
mathematical functions can only be applied to the double class. We will see later how to convert between
double and uint8.
Binary image
This image format also stores an image as a matrix but can
only color a pixel black or white (and nothing in between). It assigns a 0 for
black and a 1 for white.
Indexed image
This is a practical way of representing color images. (In
this course we will mostly work with gray scale images but once you have
learned how to work with a gray scale image you will also know the principle
how to work with color images.) An indexed image stores an image as two
matrices. The first matrix has the same size as the image and one number for
each pixel. The second matrix is called the color map and its size may
be different from the image. The numbers in the first matrix is an instruction
of what number to use in the color map matrix.
RGB image
This is another format for color images. It represents an
image with three matrices of sizes matching the image format. Each matrix corresponds
to one of the colors red, green or blue and gives an instruction of how much of
each of these colors a certain pixel should use.
Multiframe image
In some applications we want to study a sequence of images.
This is very common in biological and medical imaging where you might study a
sequence of slices of a cell. For these cases, the multiframe format is a
convenient way of working with a sequence of images. In case you choose to work
with biological imaging later on in this course, you may use this format.
How to convert between different formats
The following table shows how to convert between the
different formats given above. All these commands require the Image
processing tool box!
Image format conversion(Within the parenthesis you type the name of the image you wish to convert.)
Operation:
|
Matlab command:
|
Convert between intensity/indexed/RGB format to binary
format.
|
dither()
|
Convert between intensity format to indexed format.
|
gray2ind()
|
Convert between indexed format to intensity format.
|
ind2gray()
|
Convert between indexed format to RGB format.
|
ind2rgb()
|
Convert a regular matrix to intensity format by scaling.
|
mat2gray()
|
Convert between RGB format to intensity format.
|
rgb2gray()
|
Convert between RGB format to indexed format.
|
rgb2ind()
|
How to convert between double and uint8
When you store an image, you should store it as a uint8 image since this requires
far less memory than double.
When you are processing an image (that is performing mathematical operations on
an image) you should convert it into a double.
Converting back and forth between these classes is easy.
I=im2double(I); converts an image named I from uint8 to double.
I=im2uint8(I);
converts an image named I from double to uint8.
How to read files
When you encounter an image you want to work with, it is
usually in form of a file (for example, if you down load an image from the web,
it is usually stored as a JPEG-file). Once we are done processing an image, we
may want to write it back to a JPEG-file so that we can, for example, post the
processed image on the web. This is done using the imread and imwrite
commands. These commands require the Image processing tool box!
Reading and writing image files
Operation:
|
Matlab command:
|
Read an image.
(Within the parenthesis you type the name of the image file you wish to read. Put the file name within single quotes ' '.) |
imread()
|
Write an image to a file.
(As the first argument within the parenthesis you type the name of the image you have worked with. As a second argument within the parenthesis you type the name of the file and format that you want to write the image to. Put the file name within single quotes ' '.) |
imwrite(
, )
|
Loading and saving variables in Matlab
This section explains how to load and save variables in
Matlab. Once you have read a file, you probably convert it into an intensity
image (a matrix) and work with this matrix. Once you are done you may want to
save the matrix representing the image in order to continue to work with this
matrix at another time. This is easily done using the commands save and load. Note that save and load are commonly used Matlab
commands, and works independently of what tool boxes that are installed.
Loading and saving variables
Operation:
|
Matlab command:
|
Save the variable X
.
|
save X
|
Load the variable X
.
|
load X
|
Down load the following image (by clicking on the image
using the right mouse button) and save the file as cell1.jpg.
I=imread('cell1.jpg'); % Load the image file and store it as the variable I. whos % Type "whos" in order to find out the size and class of all stored variables. save I % Save the variable I. ls % List the files in your directory. % There should now be a file named "I.mat" in you directory % containing your variable I. |
Next we will see that we can display an image using the command imshow. This command requires the image processing tool box. Commands for displaying images will be explained in more detail in the section "How to display images in Matlab" below.
clear % Clear Matlab's memory. load I % Load the variable I that we saved above. whos % Check that it was indeed loaded. imshow(I) % Display the image I=im2double(I); % Convert the variable into double. whos % Check that the variable indeed was converted into double % The next procedure cuts out the upper left corner of the image % and stores the reduced image as Ired. for i=1:256
for j=1:256
Ired(i,j)=I(i,j);
end
end
whos % Check what variables you now have stored. imshow(Ired) % Display the reduced image. |
Example 2
Go to the CU home page
and down load the image of campus with the Rockies in the background. Save the
image as pic-home.jpg
Next, do the following in Matlab. (Make sure you are in the same directory
as your image file). clear A=imread('pic-home.jpg'); whos imshow(A) |
A=rgb2gray(A); % Convert to gray scale whos imshow(A) |
Note: In other cases when you down load a color image and type whos you might see that there is one matrix corresponding to the image size and one matrix called map stored in Matlab. In that case, you have loaded an indexed image (see section above). In order to convert the indexed image into an intensity (gray scale) image, use the ind2gray command described in the section "How to convert between different formats" above.
How to display an image in Matlab
Here are a couple of basic Matlab commands (do not require
any tool box) for displaying an image.
Displaying an image given on matrix form
Operation:
|
Matlab command:
|
Display an image represented as the matrix X.
|
imagesc(X)
|
Adjust the brightness. s is a parameter such that
-1<s<0 gives a darker image, 0<s<1 gives a brighter image. |
brighten(s)
|
Change the colors to gray.
|
colormap(gray)
|
If you are using Matlab with an Image processing tool box installed, I recommend you to use the command imshow to display an image.
Displaying an image given on matrix form (with image processing tool box)
Operation:
|
Matlab command:
|
Display an image represented as the matrix X.
|
imshow(X)
|
Zoom in (using the left and right mouse button).
|
zoom
on
|
Turn off the zoom function.
|
zoom
off
|
Exercise
Load your favorite image into Matlab (if it is on any of the
format described in the section "Image formats supported by Matlab"
above). Now experiment with this image, using the commands given in this
worksheet.
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