Image Processing Using MATLAB

Workshop on “Image processing
using MATLAB”
Presented by
Amarjeetsingh Thakur
Asst. Professor
Dept. of Electronics & Communication Engg.
S.G.B.I.T. Belgaum

Outline
 What is MATLAB?
 Image Processing tool box
 Image formats
 How to read an image?
 Image conversion
 Arithmetic operations on images
 Conversion of an image into different formats
 Image rotation
 Image blurring and deblurring
 Fill in ROI in grayscale image
 References

What is MATLAB?
• MATLAB = MATrix LABoratory
• “MATLAB is a high-level language and
interactive environment that enables us to
perform computationally intensive tasks faster
than with traditional programming languages
such as C, C++ and Fortran.”
• MATLAB is an interactive, interpreted language
that is designed for fast numerical matrix
calculations.

Key Industries
 Aerospace and defense
 Automotive
 Biotech and pharmaceutical
 Communications
 Computers
 Education
 Electronics and semiconductors
 Energy production
 Industrial automation and machinery
 Medical devices

The MATLAB Environment
MATLAB window
components:
Workspace
> Displays all the defined
variables
Command Window
> To execute commands
in the MATLAB
environment
Command History
> Displays record of the
commands used
File Editor Window
> Define functions

MATLAB Help
• MATLAB Help is an
extremely powerful
assistance to learning
MATLAB
• Help not only contains the
theoretical background,
but also shows demos for
implementation
• MATLAB Help can be
opened by using the
HELP pull-down menu

MATLAB Help (cont.)
• Any command description
can be found by typing
the command in the
search field
• As shown above, the
command to take square
root (sqrt) is searched
• We can also utilize
MATLAB Help from the
command window as
shown

What is the Image Processing
Toolbox?
• The Image Processing Toolbox is a collection of
functions that extend the capabilities of the
MATLAB’s numeric computing environment. The
toolbox supports a wide range of image
processing operations, including:
– Geometric operations
– Linear filtering and filter design
– Transforms
– Image analysis and enhancement
– Binary image operations
– Region of interest operations

Images in MATLAB
• MATLAB can import/export
several image formats:
– BMP (Microsoft Windows
Bitmap)
– GIF (Graphics
Interchange Files)
– HDF (Hierarchical Data
Format)
– JPEG (Joint Photographic
Experts Group)
– PCX (Paintbrush)
– PNG (Portable Network
Graphics)
– TIFF (Tagged Image File
Format)
• Data types in MATLAB
– Double (64-bit double-
precision floating point)
– Single (32-bit single-
precision floating point)
– Int32 (32-bit signed
integer)
– Int16 (16-bit signed
integer)
– Int8 (8-bit signed integer)
– Uint32 (32-bit unsigned
integer)
integer)

Images in MATLAB
• Binary images : {0,1}
• Intensity images : [0,1] or uint8, double etc.
• RGB images : m × n × 3
• Multidimensional images: m × n × p (p is the number of layers)

Binary Images
 They are also called “ Black & White ” images ,
containing ‘1’ for white and ‘0’(zero) for black
 MATLAB code

Intensity Images
 They are also called ‘ Gray Scale images ’ ,
containging numbers in the range of 0 to 255

Indexed Images
 These are the color images and also represented
as ‘RGB image’.
 In RGB Images there exist three indexed images.
 First image contains all the red portion of the
image, second green and third contains the blue
portion.

How to read an image??
I=imread(‘steve.jpg’)
figure
Imshow(I)
size(I) % 295 171 3

Images and Matrices
Column 1 to 256
Row1to256
o
[0, 0]
o
[256, 256]
How to build a matrix
(or image)?
Intensity Image:
row = 256;
col = 256;
img = zeros(row, col);
img(100:105, :) = 0.5;
img(:, 100:105) = 1;
figure;
imshow(img);

Image Conversion
• gray2ind - intensity image to index image
• im2bw - image to binary
• im2double - image to double precision
• im2uint8 - image to 8-bit unsigned integers
• im2uint16 - image to 16-bit unsigned integers
• ind2gray - indexed image to intensity image
• mat2gray - matrix to intensity image
• rgb2gray - RGB image to grayscale
• rgb2ind - RGB image to indexed image

Arithmetic operations on
images
1. Imadd
Syntax : Z = imadd(X,Y)
Description: Z = imadd(X,Y) adds
each element in array X with the
corresponding element in array Y and
returns the sum in the corresponding
element of the output array Z.

Figure window shows addition of
two different images

Contd..
2. imsubtract
Syntax : Z = imsubtract(X,Y)
Description: Z = imsubtract(X,Y) subtracts
each element in array Y from the
corresponding element in array X and
returns the difference in the corresponding
element of the output array Z

Figure window shows
Subtraction of two different
images

Contd..
3. immultiply
Syntax : Z = immultiply(X,Y)
Description: Z = immultiply(X,Y)
multiplies each element in array X by the
returns the product in the corresponding

Figure window shows
Multiplication of two different
images

Contd..
4. imdivide
Syntax : Z = immultiply(X,Y)
Description: Z = imdivide(X,Y) divides
each element in the array X by the
returns the result in the corresponding

Figure window shows Division of
two different images

Converting RGB image to
gray format
I=imread(‘Sachin.jpg'); % Read an
image
I=rgb2gray(I); % RGB to gray
conversion
figure % Figure window
imshow(I) % Display figure on
figure window

Figure window displaying
Gray image

RGB to BW image conversion
commands
 i=imread('Sachin.jpg');
 I=im2bw(I);
 imshow(I)

Image rotation by some angle
 I=imread('steve.jpg');
 J=imrotate(I,45); % Rotate image
anticlockwise by
an angle 45
 K=imrotate(I,-45); % Rotate image
clockwise by
an angle 45
 imshow(J)
 Imshow(K)

Deblurring operation on an
blurred image using wiener filter
 I=imread(‘Sachin.jpg');
 figure
 imshow(I)

Commands for blurring the
image
 PSF=fspecial('motion');
 Blurred=imfilter(I,PSF,'circular','con
v');
 figure, imshow(Blurred)

Commands for deblurring the
image
 wnr1=deconvwnr(Blurred,PSF);
 figure, imshow(wnr1);
 title('Restored image');

Fill in specified region of interest
(ROI) polygon in grayscale
image
I = imread('eight.tif');
J = roifill(I);
figure, imshow(J)

ROI fill
I=imread('C:Documents and
SettingsAll
Users.WINDOWSDocumentsMy
PicturesSample
Picturesavataar.jpg');
figure
imshow(I)
J=rgb2gray(I);
figure
imshow(J)

ROI fill (contd..)
 K = roifill(J);
 figure
 imshow(K)

Applications of image
processing
 BIOLOGICAL: automated systems for analysis of
samples.
 DEFENSE/INTELLIGENCE: enhancement and
interpretation of images to find and track targets.
 DOCUMENT PROCESSING: scanning, archiving,
transmission.
 FACTORY AUTOMATION: visual inspection of
products.
• MATERIALS TESTING: detection and quantification
of cracks, impurities, etc.
 MEDICAL: disease detection and monitoring,
therapy/surgery planning

References
 www.mathworks.com
 “Digital Image Processing using
MATLAB” by Rafael C. Gonzalez,
Richard E. Woods, Steven L. Eddins.

Image Processing Using MATLAB

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