3.1.10. IMAGE TYPES AND
DATA CLASSES
MATLAB supports intensity images (including binary images),
indexed images, and color images. Most of this class will involve intensity
images, in which each pixel is a scalar measure of light by a detector. Binary
images have only values 0 or 1 for 'no light' or 'light'. Binary pixel values
can be represented with one bit and packed into bytes.
Intensity images with more precision can use integer data of
various lengths or floating point data of various lengths. Typically, we will
use uint8 (unsigned 8-bit integers) or double (double-precision floating point
numbers).
MATLAB has functions for converting the data types of
images. It is important to understand the mathematics of these
conversions.
Image Indexing
The value of a single pixel in an image is indexed as:
p(m,n)
Multiple pixels can be indexed with a vector as:
p([1 3],1) which selects pixels p(1,1) and p(3,1).
Multiple pixels also can be indexed using a range. The
first row is all pixels with row index 1 and any column index:
p(1,1:N) The notation 1:N indicates all indexes from 1 to N.
The notation can contain a step value, e.g., 1::2:5 indicates
indexes 1, 3, 5.
The keyword end can be used to indicate the last index. The range
operator by itself, :, indicates the full range, from 1 to end either of a
dimension or of the whole matrix.
Standard Arrays
MATLAB has standard arrays for creating arrays of a defined
size with zeros, ones, true, false, or random values. For example:
p = zeros(M,N);
Builtin Functions
MATLAB has many useful builtin functions listed in Appendix A
of the text book. For example:
max(p(:))
Gives the largest value in the matrix.
Note, given matrix p, max(p) treats the matrix as an array of
column vectors and returns a vector of the largest value in each column.
Operators
MATLAB has builtin arithmetic, relational, and logical
operators.
Two images can be added so that the output value at each pixel
is the sum of the values of the corresponding input pixels. For
example:
r = p+q;
which also can be written:
r = plus(p,q);
The operands can be scalars, vectors, or matrices.
The array and matrix arithmetic operations are done in double
precision floating point. MATLAB also provides operations which support
integer arithmetic, e.g., imadd and immultiply,
For multiplication and some other operations, MATLAB has two
types of arithmetic operations: array arithmetic operations which perform the
operation on corresponding pixels of the input images and matrix arithmetic
operations which perform matrix arithmetic.
Array multiplication is:
r = p.*q;
or
r = times(p,q);
Matrix multiplication is:
r = p*q;
or
r = mtimes(p,q);
3.1.11. IMAGE FORMATS SUPPORTED BY MATLAB
The following image formats are supported by Matlab:
BMP, HDF, JPEG, PCX, XWD and TIFF.
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.
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