Matlab Image You Might Not Have Read Permission.

Introduction to paradigm processing in Matlab 1

by Kristian Sandberg, Department of Applied Mathematics, University of Colorado at Boulder

Introduction

This worksheet is an introduction on how to handle images in Matlab. When working with images in Matlab, there are many things to keep in mind such as loading an prototype, using the right format, saving the data as different data types, how to display an image, conversion between different image formats, etc. This worksheet presents some of the commands designed for these operations. Nearly of these commands require you to have the Epitome processing tool box installed with Matlab. To find out if information technology is installed, blazon ver at the Matlab prompt. This gives you a list of what tool boxes that are installed on your system.

For further reference on image handling in Matlab you are recommended to use Matlab's help browser. There is an extensive (and quite expert) on-line transmission for the Paradigm processing tool box that yous tin access via Matlab's help browser.

The first sections of this worksheet are quite heavy. The only manner to understand how the presented commands piece of work, is to carefully work through the examples given at the stop of the worksheet. One time you can get these examples to work, experiment on your own using your favorite image!

Fundamentals

A digital image is equanimous of pixels which can exist thought of as minor dots on the screen. A digital image is an instruction of how to color each pixel. We will see in item afterward on how this is done in practice. A typical size of an prototype is 512-by-512 pixels. Later in the course you volition meet that information technology is convenient to let the dimensions of the image to be a power of 2. For example, 2^ix=512. In the general example we say that an image is of size 1000-by-northward if it is composed of 1000 pixels in the vertical direction and due north pixels in the horizontal management.

Let us say that we have an image on the format 512-by-1024 pixels. This means that the data for the epitome must contain information virtually 524288 pixels, which requires a lot of memory! Hence, compressing images is essential for efficient paradigm processing. You will afterward meet how Fourier analysis and Wavelet analysis can assist united states to compress an image significantly. In that location are likewise a few "computer scientific" tricks (for instance entropy coding) to reduce the amount of data required to shop an paradigm.

Image formats supported past Matlab

The following image formats are supported by Matlab:

• BMP
• HDF
• JPEG
• PCX
• TIFF
• XWB

Well-nigh images you find on the Internet are JPEG-images which is the name for one of the virtually widely used compression standards for images. If y'all accept stored an image you tin can normally 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 volition see later on that we can load an image of this format into Matlab.

Working formats in Matlab

If an image is stored equally a JPEG-image on your disc we starting time read it into Matlab. Withal, in order to kickoff working with an image, for case perform a wavelet transform on the prototype, nosotros must catechumen it into a different format. This section explains 4 common formats.

Intensity epitome (gray calibration image)

This is the equivalent to a "gray calibration epitome" and this is the image nosotros volition mostly work with in this course. It represents an image as a matrix where every element has a value corresponding to how vivid/dark the pixel at the corresponding position should be colored. There are 2 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") betwixt 0 and i to each pixel. The value 0 corresponds to black and the value 1 corresponds to white. The other grade is chosen uint8 which assigns an integer between 0 and 255 to stand for the brightness of a pixel. The value 0 corresponds to blackness and 255 to white. The class uint8 but requires roughly 1/8 of the storage compared to the class double. On the other hand, many mathematical functions tin can only exist applied to the double class. We will see later how to convert between double and uint8.

Binary image

This prototype format likewise stores an prototype as a matrix but can merely color a pixel blackness or white (and nothing in betwixt). Information technology assigns a 0 for black and a 1 for white.

Indexed image

This is a practical way of representing color images. (In this class we volition mostly work with gray calibration images but once y'all have learned how to piece of work with a gray scale prototype you will also know the principle how to piece of work with color images.) An indexed image stores an epitome as two matrices. The beginning matrix has the aforementioned size as the paradigm and one number for each pixel. The second matrix is called the colour map and its size may exist different from the prototype. The numbers in the first matrix is an instruction of what number to apply in the color map matrix.

RGB image

This is some other format for colour images. It represents an image with three matrices of sizes matching the image format. Each matrix corresponds to one of the colors ruby-red, green or bluish and gives an didactics of how much of each of these colors a certain pixel should use.

Multiframe image

In some applications we desire to study a sequence of images. This is very mutual in biological and medical imaging where yous might written report a sequence of slices of a prison cell. For these cases, the multiframe format is a convenient way of working with a sequence of images. In case you cull to work with biological imaging subsequently on in this course, you may utilize this format.

How to catechumen between different formats

The following table shows how to convert between the dissimilar formats given higher up. All these commands require the Image processing tool box!

Epitome format conversion
(Inside the parenthesis you type the name of the image y'all wish to convert.)

Operation:	Matlab command:
Convert between intensity/indexed/RGB format to binary format.	dither()
Catechumen between intensity format to indexed format.	gray2ind()
Convert between indexed format to intensity format.	ind2gray()
Catechumen 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 betwixt RGB format to indexed format.	rgb2ind()

The command mat2gray is useful if yous have a matrix representing an prototype just the values representing the gray scale range between, let'southward say, 0 and thousand. The control mat2gray automatically re scales all entries then that they autumn within 0 and 255 (if you use the uint8 class) or 0 and 1 (if you use the double class).

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 epitome) you should convert it into a double. Converting back and forth between these classes is like shooting fish in a barrel.

I=im2double(I);

converts an prototype named I from uint8 to double.

I=im2uint8(I);

converts an image named I from double to uint8.

How to read files

When you come across an image you want to work with, it is normally in class of a file (for example, if you down load an image from the spider web, information technology is usually stored equally a JPEG-file). One time we are washed processing an epitome, we may want to write it back to a JPEG-file so that nosotros can, for example, mail the processed paradigm on the web. This is done using the imread and imwrite commands. These commands require the Image processing tool box!

Reading and writing paradigm files

Functioning:	Matlab command:
Read an image. (Within the parenthesis you type the name of the epitome file you wish to read. Put the file proper noun inside single quotes ' '.)	imread()
Write an prototype to a file. (As the first statement inside the parenthesis you type the name of the image you lot have worked with. Every bit a second argument within the parenthesis y'all blazon the proper noun of the file and format that you lot desire to write the image to. Put the file proper name within single quotes ' '.)	imwrite( , )

Brand sure to use semi-colon ; after these commands, otherwise yous volition become LOTS OF number scrolling on yous screen... The commands imread and imwrite support the formats given in the department "Prototype formats supported by Matlab" to a higher place.

Loading and saving variables in Matlab

This section explains how to load and save variables in Matlab. Once yous have read a file, you probably convert it into an intensity image (a matrix) and piece of work with this matrix. Once yous are done you may want to relieve the matrix representing the image in gild to proceed to piece of work with this matrix at another time. This is easily washed 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 .	relieve X
Load the variable 10 .	load X

Examples

In the first example we will downwards load an prototype from the spider web, read it into Matlab, investigate its format and save the matrix representing the image.

Example 1.

Down load the following image (by clicking on the image using the right mouse button) and save the file as cell1.jpg.

This is an image of a cell taken past an electron microscope at the Department of Molecular, Cellular and Developmental Biological science at CU.

At present open Matla and make sure you are in the same directory as your stored file. (You can check what files your directory contains by typing ls at the Matlab prompt. Yous alter directory using the control cd.) At present type in the following commands and run across what each control does. (Of course, you do non accept to type in the comments given in the code after the % signs.)

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 course of all stored variables.

salve I % Relieve the variable I.

ls % List the files in your directory.

% There should now be a file named "I.mat" in y'all directory
% containing your variable I.

Note that all variables that yous save in Matlab usually get the suffix .mat.

Side by side nosotros volition see that we can display an prototype using the control imshow. This command requires the paradigm processing tool box. Commands for displaying images will exist explained in more than detail in the department "How to display images in Matlab" below.

clear % Clear Matlab's memory.

load I % Load the variable I that we saved higher up.

whos % Bank check that information technology was indeed loaded.

imshow(I) % Display the image

I=im2double(I); % Catechumen the variable into double.

whos % Check that the variable indeed was converted into double

% The next process cuts out the upper left corner of the prototype
% and stores the reduced image as Ired.

for i=i:256

for j=1:256

Ired(i,j)=I(i,j);

cease

end

whos % Check what variables you at present have stored.

imshow(Ired) % Display the reduced image.

Example two

Become to the CU home folio and downwardly load the prototype of campus with the Rockies in the background. Save the image as pic-home.jpg

Side by side, exercise the following in Matlab. (Brand certain you are in the aforementioned directory as your paradigm file).

articulate

A=imread('moving picture-home.jpg');

whos

imshow(A)

Note that when you typed whos it probably said that the size was 300x504x3. This ways that the epitome was loaded as an RGB image (see the section "RGB image above"). However, in this course we volition mostly work with gray scale images, so allow us catechumen information technology into a gray scale (or "intensity") image.

A=rgb2gray(A); % Convert to gray scale

whos

imshow(A)

Now the size indicates that our image is nada else than a regular matrix.

Note: In other cases when you down load a color image and type whos you might see that there is ane matrix corresponding to the image size and ane matrix called map stored in Matlab. In that example, you have loaded an indexed image (run into department higher up). In order to convert the indexed epitome into an intensity (gray scale) image, utilize the ind2gray command described in the section "How to convert between dissimilar formats" above.

How to display an epitome in Matlab

Here are a couple of basic Matlab commands (do non require any tool box) for displaying an image.

Displaying an paradigm given on matrix form

Operation:	Matlab command:
Display an paradigm represented every bit the matrix X.	imagesc(X)
Adapt the effulgence. s is a parameter such that -1<due south<0 gives a darker image, 0<south<1 gives a brighter image.	brighten(s)
Change the colors to grey.	colormap(gray)

Sometimes your epitome may not be displayed in gray scale fifty-fifty though you might have converted it into a gray scale image. Yous can then apply the command colormap(gray) to "force" Matlab to use a grey calibration when displaying an paradigm.

If you are using Matlab with an Prototype processing tool box installed, I recommend yous to use the control imshow to brandish an image.

Displaying an epitome given on matrix form (with prototype processing tool box)

Operation:	Matlab control:
Display an paradigm represented as the matrix 10.	imshow(X)
Zoom in (using the left and right mouse button).	zoom on
Turn off the zoom function.	zoom off

Exercise

Load your favorite epitome into Matlab (if it is on whatever of the format described in the department "Epitome formats supported by Matlab" higher up). Now experiment with this image, using the commands given in this worksheet.

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Source: https://webpage.pace.edu/kt52695n/improcessing/matlabimpr.html