1. Digital Image Processing
With
Matlab
Prof. Muhammad Saeed

2. Contents Fundamentals Spatial Domain Processing
Frequency Domain Processing
Color Image Processing
Image Restoration
Image Compression
Morphological Image Processing
Image Segmentation
Representation and Description
Object Recognition
MS Image Processing

3. Introduction to Image:
Fundamentals
Introduction to Image:
Image as matrix
Image Type
Image Class
Image Size
Image Resolution
Filename: [1x64 char]
FileModDate: '24-Oct :51:40'
FileSize: 48613
Format: 'jpg'
FormatVersion: ‘'
Width: 344
Height: 401
BitDepth: 24
ColorType: 'truecolor'
FormatSignature: ''
NumberOfSamples: 3
CodingMethod: 'Huffman'
CodingProcess: 'Sequential'
Comment: {}
ImageDescription: 'OLYMPUS DIGITAL CAMERA’
Make: 'OLYMPUS OPTICAL CO.,LTD'
Model: 'C4040Z'
Orientation: 1
Software: 'v552u-A80'
DateTime: '2001:01:02 06:01:42'
YCbCrPositioning: 'Co-sited'
DigitalCamera: [1x1 struct]
UnknownTags: [3x1 struct]
Read:
kid=imread('kid02.jpg');
Image Info:
info=imfinfo('kid02.jpg'); or
whos kid;
[r c]=size(kid);
Display:
figure;
imshow(kid);
imshow(kid, [low high]);
MS Image Processing

4. Conversion of Class and Type:
Write on Disk:
imwrite(kid,'kid02gray.png');
imwrite(kid,'kid02gray.jpg’,’quality’,75); % (0:100)
imwrite(kid,'kid02gray.tif’,’compression’,’none’,‘resolution’,[m n]);
% (packbits, ccitt), (dpi)
Conversion of Class and Type:
B=logical(A);
C=im2bw(T);
D=mat2gray(P,[Pmin Pmax]);
F=Im2double(g);
G=im2uint8(H);
Image Arithmatic:
C=imadd(A,B); C=imsubtract(A,B);
C=immultiply(A,B); C=imdivide(A,B);
C=imabsdiff(A,B); C=imcomplement(A);
C=imlincomb(A,B);
MS Image Processing

5. Image Flipping and Sizing:
C=F(end:-1:1,:);
C=F(:, end:-1:1);
C=F(1:2:end,1:2:end);
Image Cropping:
C=F(50:end-20, 50:end-40);
Pixel Information Direct:
impixelinfo;
imdistline;
Color Image to Gray:
C=rgb2gray(F);
MS Image Processing

6. Spatial Domain Processing
Intensity Transformation:
S=imadjust(A,[inL inH],[outL outH],gamma);
S=imadjust(A,[0 1],[1 0]); %image complement
S=imadjust(A,[],[],2);
Logarithmic and Contrast Stretching Transform i)
ii)
Histograms
H=imhist(A,nbins);
H=imhist(A,nbins)/numel(A); % normalized histogram
H=histeq(A,nbins); % histogram equalization
MS Image Processing

7. Linear Spatial Filtering:
C=imfilter(A, w, ‘mode’, ‘boundary opts’, ‘size opts’);
% w is the filter
% modes= corr, conv
% boundary options=p, replicate, symmetric, circular
% size options=full, same
w=fspecial(‘type’, parameters);
type parameters default
average [r, c] 3x3
disk R (radius, square of 2R+1) 5
gaussian [r, c],sig(STD) 3x3, 0.5
laplacian(3x3) alpha(0-1) 0.5
log(laplacian [r, c],sig(STD) 5x5, 0.5
of a gaussian)
motion len(pixel), theta(degrees)CCW 9, 0
prewitt(3x3, verticle gradient, transpose for horizontal)
sobel(3x3, verticle gradient, transpose for horizontal)
unsharp(3x3) alpha(0-1) 0.2
MS Image Processing

8. Mathematical Form of Filters:
Sobel: I,
and II,
Prewitt: I,
and II,
Gaussian Filter:
Laplacian Filter:
Unsharp Filter:
MS Image Processing

9. Linear Spatial Filters in action:1.
kid=imread('kid02.jpg');
w=[1 1 1;1 -8 1;1 1 1];
C=imfilter(kid, w,'corr', 'replicate', 'same');
subplot(1,2,1);imshow(kid);title('Original','FontSize',20);
subplot(1,2,2);imshow(4*C);title(mat2str(w),'FontSize',20); 2.
chboard=(checkerboard(90)>0.1);
w=[0 1 0;1 -4 1;0 1 0];
C=imfilter(chboard, w,'conv','replicate', 'full');
subplot(1,2,1);imshow(chboard);title('Original','FontSize',20);
subplot(1,2,2);imshow(4*C);title(mat2str(w),'FontSize',20); 3.
kid=imread('kid02.jpg');
w=fspecial('average',[10 10]);
C=imfilter(kid, w,'conv','replicate','same'); subplot(1,2,1);imshow(kid);title('Original','FontSize',20);
subplot(1,2,2);imshow(C);title('Average','FontSize',20);
average
MS Image Processing

10. subplot(1,2,1);imshow(kid);title('Original','FontSize',20); 4.
kid=imread('kid02.jpg');
w=fspecial('disk', 6);
C=imfilter(kid, w);
subplot(1,2,1);imshow(kid);title('Original','FontSize',20);
subplot(1,2,2);imshow(C);title('Disk','FontSize',20);
disk 5.
kid=imread('kid02.jpg');
w=fspecial('gaussian',10, 0.8);
C=imfilter(kid, w);
subplot(1,2,1);imshow(kid);title('Original','FontSize',20);
subplot(1,2,2);imshow(C);title('Gaussian','FontSize',20);
gaussian 6.
kid=imread('kid02.jpg');
w=fspecial('laplacian', 0.6);
C=imfilter(kid, w,'conv','replicate','same'); subplot(1,2,1);imshow(kid);title('Original','FontSize',20);
subplot(1,2,2);imshow(6*C);title('Laplacian','FontSize',20);
laplacian
MS Image Processing

11. C=imfilter(kid, w,'conv','replicate','same'); 7.
kid=imread('kid02.jpg');
w=fspecial('log',5,0.2);
C=imfilter(kid, w,'conv','replicate','same');
subplot(1,2,1);imshow(kid);title('Original','FontSize',20);
subplot(1,2,2);imshow(4*C);title('Log','FontSize',20);
log 8.
kid=imread('kid02.jpg');
w=fspecial('motion',20,10);
C=imfilter(kid, w);
subplot(1,2,1);imshow(kid);title('Original','FontSize',20);
subplot(1,2,2);imshow(C);title('Motion','FontSize',20);
motion 9.
kid=imread('kid02.jpg');
w=fspecial('prewitt');
C=imfilter(kid, w);
subplot(1,2,1);imshow(kid);title('Original','FontSize',20);
subplot(1,2,2);imshow(C);title('Prewitt','FontSize',20);
prewitt
MS Image Processing

12. subplot(1,2,1);imshow(kid);title('Original','FontSize',20);
10.
kid = imread('kid02.jpg');
w = fspecial('sobel');
C=imfilter(kid, w);
subplot(1,2,1);imshow(kid);title('Original','FontSize',20);
subplot(1,2,2);imshow(C);title('Sobel','FontSize',20);
sobel
11.
kid = imread('kid02.jpg');
w = fspecial('unsharp',0.5);
C=imfilter(kid, w);
subplot(1,2,1);imshow(kid),title('Original','FontSize',20);
subplot(1,2,2);imshow(C),title('Unsharp','FontSize',20);
unsharp
12.
kid=imread('kid02Gray.jpg');
C=filter2('sobel',kid);
subplot(1,2,1);imshow(kid);title('Original','FontSize',20);
subplot(1,2,2);imshow(C,[]);title('Filtered Red','FontSize',20);
filter2
MS Image Processing

13. kid=imread('kid02darkgray.jpg'); subplot(2,2,1);
13.
kid=imread('kid02darkgray.jpg');
subplot(2,2,1);
imshow(kid);title('Original','FontSize',20);
subplot(2,2,2);
imhist(kid,64);title('Histogram I','FontSize',20);
subplot(2,2,3);
heq=histeq(kid,64); imshow(heq,[]);
title(‘Equalized','FontSize',20);
subplot(2,2,4);
imhist(heq,64); title('Eq. Histogram','FontSize',20);
histeq
MS Image Processing

14. Nonlinear Spatial Filters:
ordfilt2 (f, order, domain)
kid=imread('kid02darkgray.jpg');
kidfilt=ordfilt2(kid,25,true(7));
subplot(1,2,1);imshow(kid); title('Original','FontSize',20);
subplot(1,2,2);imshow(kidfilt); title(‘Filtered','FontSize',20);
2) medfilt2 (f, ’symmetric’)
kid=imread('kid02Gray.jpg');
kidnoise=imnoise(kid,'salt & pepper',0.2);
kid2=medfilt2(kidnoise,[5 5],'symmetric'); %symmetric, zeros, indexed
%kid2=medfilt2(kid2,'symmetric');
subplot(1,2,1);imshow(kidnoise);title('Original','FontSize',20);
subplot(1,2,2);imshow(kid2);title('Filtered','FontSize',20);
MS Image Processing

15. END
MS Image Processing