DIP Labmanual

8/12/2019 DIP Labmanual

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Digital Image Processing

Lab Manual

Submitted By :-

Prateek Bansal

2K10/EC/100


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Experiment -1

Objective :To perform image enhancement operations.

1. Contrast Stretching2. Image Negative

Platform Used: MatlabR2011b

Code:

I=imread('lena.tiff');[m n]=size(I);I=rgb2gray(I);I=imresize(I,[256 256]);figure(1);imshow(I);Negative=255-I; %Image Negativefigure(2);imshow(Negative);

contrast=1./(1+(256./double(I)).^5); %Contrast Stretchingfigure(3);imshow(contrast);


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OUTPUT:

Original Image

Image Negative

Contrast Stretching


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Experiment 2

Objective: To flip an image horizontally and vertically.

Platform Used:MatlabR2011b

Theory:

Flip: In flipdim(A,dim) When the value of dim is 1, the array is flipped row-wise down that results in

vertical flip. When dim is 2, the array is flipped columnwise left to right. flipdim(A,1)is the same

as flipud(A), and flipdim(A,2) is the same as fliplr(A).

Code:

% flip operation

I = imread('onion.png');I2 = flipdim(I ,2); %# horizontal flip

I3 = flipdim(I ,1); %# vertical flipI4 = flipdim(I3,2); %# horizontal+vertical flipsubplot(2,2,1), imshow(I);subplot(2,2,2), imshow(I2);subplot(2,2,3), imshow(I3);subplot(2,2,4), imshow(I4);


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Output


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Experiment 3

Objective: To perform average filtering of a noisy image.

Platform Used:Matlab

Theory:

In average filter a mask of NXN size is used to move over an image. The corresponding pixel of image tothe central pixel position of mask is replaced by an average of all NXN pixels overlapping with mask.This method is used to remove the noise (generally Gaussian type) .The value of N can be 3,5,.

Code:

%implementation of arthimetic mean filterclc;close all;a=im2double(rgb2gray(imread('lena.tiff')));subplot(2,2,1)imshow(a);title('original image')a=imnoise(a,'gaussian',0,0.005);subplot(2,2,2)imshow(a)title('degraded imgae')b=(1/9)*ones(3);[m n]=size(a);fori=1:m-2forj=1:n-2

a(i,j)=sum(sum((a(i:i+2,j:j+2).*b)));endendsubplot(2,2,3)

imshow(a)title('restored image')


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Experiment 4

Objective: To perform histogram equalization on an image.


Theory:

Histogram equalization is used to enhance the contrast of the image. It uniformly distributes the values of

intensities over the pixels. Dark region becomes darker and bright region becomes brighter, that

improves the appearance of the image.

Code:

%program for histogram equalizationclc;close all;a=rgb2gray(imread('Penguins.jpg'));% read color image and convert into gray

image[m n]=size(a); %size of imageL=255; % maximum possible gray levelsubplot(2,1,1)imshow(a) %display original imagetitle('original image')fori=0:L-1p(i+1)=length(find(a==i))/(m*n); % find the probabilities of each gray levelendfori=1:mforj=1:n

c=a(i,j);b(i,j)=sum(p(1:c+1)); % sum up the probabilities of pixels (equalization)endendsubplot(2,1,2)imshow(b)title('histogram equalized image')


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OUTPUT:


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Experiment 5

Objective: To implement sobel, Canny edge detection on an image.


Code:

A=imread('lena.tiff');subplot(2,2,1),imshow(A);

B=rgb2gray(A);B=imresize(B,[256 256]);C=double(B);

fori=1:size(C,1)-2forj=1:size(C,2)-2%Sobel mask for x-direction:

Gx=((2*C(i+2,j+1)+C(i+2,j)+C(i+2,j+2))-(2*C(i,j+1)+C(i,j)+C(i,j+2)));

%Sobel mask for y-direction:Gy=((2*C(i+1,j+2)+C(i,j+2)+C(i+2,j+2))-(2*C(i+1,j)+C(i,j)+C(i+2,j)));

%The gradient of the image%B(i,j)=abs(Gx)+abs(Gy);B(i,j)=sqrt(Gx.^2+Gy.^2);

endend

subplot(2,2,2),imshow(B); title('Sobel gradient');

%Define a threshold valueThresh=100;B=max(B,Thresh);B(B==round(Thresh))=0;

B=uint8(B);

subplot(2,2,3),imshow(B);title('Edge detected Image');

% Canny Edge detectionI = imread('lena.tiff');

I=rgb2gray(I);I=imresize(I,[256 256]);th=input('Enter the threshold limit=');% th is upper threshold and lower threshold is .4*upper threshold% The value of threshold should be less than 1I2 = edge(I,'canny',th);figure(2);imshow(I2);title('Canny edge detected output ');


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Output:

Enter the threshold limit=0.25


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Experiment 6

Objective: To perform deblurring of an image using Weiner filter.


Theory:Weiner2 performs 2-D adaptive noise filtering.

wiener2lowpass-filters a grayscale image that has been degraded by constant power additive noise.wiener2 uses a pixelwise adaptive Wiener method based on statistics estimated from a local neighborhood

of each pixel ie. The wiener2 function applies a Wiener filter (a type of linear filter) to an imageadaptively. Where the variance is large, wiener2 performs little smoothing. Where the variance is small,

wiener2 performs more smoothing.

This approach often produces better results than linear filtering. The adaptive filter is more selective thana comparable linear filter, preserving edges and other high-frequency parts of an image..wiener2,however, does require more computation time than linear filtering.

wiener2 works best when the noise is constant-power ("white") additive noise, such as Gaussian noise.

wiener2 estimates the local mean and variance around each pixel,

where is the N-by-Mlocal neighborhood of each pixel in the image A. wiener2 then creates a pixelwise

Wiener filter using these estimates,

where 2is the noise variance. If the noise variance is not given, wiener2 uses the average of all the local

estimated variances.

Wiener filter can be applied on an image to deblur(image restoration) it. deconvwnr(I,PSF,NSR)

function is used to deblur the image I, PSF is a point Spread Function with which I was convolved, NSRis a noise to signal ratio.

Code:

RGB = imread('saturn.png');I = rgb2gray(RGB);J = imnoise(I,'gaussian',0,0.025);subplot(2,1,1),imshow(J);K = wiener2(J,[5 5]);subplot(2,1,2), imshow(K),title('filtered image');


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Output:


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Experiment 7

Objective: To apply Median filter to an image.

Platform Used:Matlab

Theory:Median filtering is a nonlinear process useful in reducing impulsive, or salt-and-pepper noise. It

is also useful in preserving edges in an image while reducing random noise. Impulsive or salt-and pepper

noise can occur due to a random bit error in a communication channel. In a median filter, a window slides

along the image, and the median intensity value of the pixels within the window becomes the output

intensity of the pixel being processed

Like lowpass filtering, median filtering smoothes the image and is thus useful in reducing noise. Unlike

lowpass filtering, median filtering can preserve discontinuities in a step function and can smooth a few

pixels whose values differ significantly from their surroundings without affecting the other pixels

If the two impulsive values are due to noise, the result of using a median filter will be the reduce the

noise. If the two values are part of the signal, however, using the median filter will distort the image.

Code:

I=imread('lena.tiff');I=rgb2gray(I);I=imresize(I,[256 256]);subplot(2,3,1);imshow(I);title('Original Image');

I=imnoise(I,'salt & pepper');subplot(2,3,2);imshow(I);

title('Degraded Image');

%Median filtering for 3X3 windowI_3=medfilt2(I,[3 3]);subplot(2,3,3);imshow(I_3);title('Filtered Image (3X3)');




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Output


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Experiment 8

Objective: To implement Discrete Cosine Transform(DCT) of an image.


Code

%Discrete Cosine Transform (DCT)

clc;

close all

clear all;

a=imread('lena1.jpg');

a=im2double(rgb2gray(a));

a=imresize(a,[256,256]);

imshow(a);

N=256;

for k=0:N-1

for n=0:N-1

if k==0

C(k+1,n+1)=sqrt(1/N);

else

C(k+1,n+1)=sqrt(2/N)*cos((pi*(2*n+1)*k)/(2*N));

end

end

end

b=C*a*C';

figure

imshow(b);

%Reconstruction of Image

d=C'*b*C

figure

imshow(d)


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Output

Original Image Transformed Image

Reconstructed Image


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Experiment 9

Objective: To implement Hadamard Transform(DCT) of an image.


Code%Kronecker Product Function

function [B]=kronecker_product(A)

B=[A A; A -A];

end

%Hadammard Transform

clc;

close all;

clear all;

a=imread('lena.jpg');

a=imresize(a,[128,128]);

a=im2double(a);

figure

imshow(a);

%n=size(a);

h=[1/sqrt(2) 1/sqrt(2);1/sqrt(2) -1/sqrt(2)];

for i=1:log2(128)-1

h=1/sqrt(2)*kronecker_product(h);

end

h

b=h*a*h';

figure

imshow(b);

%Reconstruction

c=h'*b*h;

figure

imshow(c);


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OUTPUT:

Original Image Transformed Image Reconstructed Image


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Experiment 10

Objective: To perform dilation and erosion of an image.


Theory

Erosion and Dilation:Morphological operations apply a structuring element to an input image, creating anoutput image of the same size. In dilation the value of the output pixel is the maximumvalue of all the

pixels in the input pixel's neighborhood. In a binary image, if any of the pixels is set to the value 1, theoutput pixel is set to 1.In Erosion the value of output pixel is minimum value of all the pixels in the input

pixels neighborhood. In binary image, if any of thepixel is set to 0, the output pixel is set to 0.

Code

I = imread('lena.tiff');I=rgb2gray(I);figure(1);subplot(2,2,1);

imshow(I);title('Original Image');

%erode operation%If IM is logical and the structuring element is flat, imerode performs%binary erosion; otherwise it performs grayscale erosion.%If SE is an array of structuring element objects, imerode performs multipleerosions%of the input image, using each structuring element in SE in successionSE=strel('line',10,45);

IM2 = imerode(I,SE);

subplot(2,2,3);imshow(IM2);title('Eroded Image');

%dialation operationBW2 = imdilate(I,SE);subplot(2,2,4);imshow(BW2);title('Dilated Image');


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Output

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