The document discusses a workshop on image processing using MATLAB. It provides an overview of MATLAB and its image processing toolbox. It describes how to read, display, and convert between different image formats in MATLAB. It also demonstrates various image processing operations that can be performed, such as arithmetic operations, conversion between color and grayscale, image rotation, blurring and deblurring, and filling regions of interest. The document aims to introduce the basics of working with images in the MATLAB environment.

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

2. 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

3. 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.

4. Who uses MATLAB?

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

6. 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

7. 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

8. 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

9. 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

10. 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)
– Uint16 (16-bit unsigned
integer)
– Uint8 (8-bit unsigned

11. 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)

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

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

14. 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.

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

17. 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);

18. 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

19. 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.

20. Figure window shows addition of
two different images

21. 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

22. Figure window shows
Subtraction of two different
images

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

24. Figure window shows
Multiplication of two different
images

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

26. Figure window shows Division of
two different images

27. 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

28. Figure window displaying
Gray image

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

30. Figure window shows BW
image

31. 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)

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

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

35. Blurred image

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

37. Recovered image

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

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

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

45. ROI fill (contd..)

46. ROI fill (contd..)

47. 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

48. ANY QUERRIES????????

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

50. THANK YOU