Description

1) Take an RGB image (Lab9_3.jpg). and do the following:1. Insert Gaussian noise on its red component and Salt & pepper noise on its green and blue components.2. Concatenating the three components into an image (noisy image).3. Apply Average Filtering on noisy image from part2.4. Concatenating the three components into an image (filtered image in separate).6. Show the results2) Repeat the first part by using your own image. Report all the challenges that faced you in the process of apply noise to an image and then filtering it back..Make sure to include brief :IntroductionObjectivesConclusion

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Computer Vision and Image Processing

COMP4500

Module 4

Module 4 Lab Exercise

1.2 Color Image Processing

1.2.1 RGB Images

M *N*3 array of color pixels as a stack of three gray-scale images

Let fR, fG, fB represent three RGB component images. An RGB image is formed

from these images by using the cat(concatenate) operator to stack the image.

rgb_image= cat(3 , fR, fG, fB)

The following commands extract the three component image:

fR= rgb_image(: , : , 1);

fG= rgb_image(: , : , 2);

fB= rgb_image(: , : , 3);

1.2.2 Smoothing and Sharpening Filtering Techniques on Color images

Filtering an RGB color image ,fc, with a linear spatial filter consists of the following

steps:

1. Extract the three component images:

>>fR= fc( : , : , 1);

fG= fc( : , : , 2);

fB= fc( : , : , 3);

2. Filter each component images individually. Letting w represent a filter

generated using fspecial, we smooth the red component images as follows:

>> fR_filtered= imfilter(fR, w);

3. Reconstruct the filter RGB

images:

>>fc_filtered= cat(3 , fR_filtered, fG_filtered, fB_filtered);

1. Exercises

Exercise1: Median Filter and Adaptive Median Filter

COMP4500

FACULTY OF ENGINEERING – SOHAR

UNIVERSITY

Exercise1: RGB Images

%%ex1.m%%%

clc;

clear all; close all;

I=imread(‘Lab9_2.png’

); size(I);

%251x366x3

R=I(:,:,1);

G=I(:,:,2);

B=I(:,:,3);

rgb_image= cat(3 , R, G, B);

subplot(121),imshow(I),title(‘original image’);

subplot(122),imshow(rgb_image),title(‘reconstructed image’);

Output:

[Type here]

COMP4500

FACULTY OF ENGINEERING – SOHAR

UNIVERSITY

Exercise2: Smoothing Filtering on Color images

%%ex2.m%%%

clc;

clear all; close all;

a=imread(‘Lab9_3.jpg’);

% Separating red,green and blue components of image

r=a(:,:,1); g=a(:,:,2);

b=a(:,:,3);

% Adding Gaussian noise on green component

g1=imnoise(g,’gaussian’);

% Adding Salt & pepper noise on red

component r1=imnoise(r,’salt & pepper’,0.2);

% Concatenating red,green and blue components to form a composite

image.

image=cat(3,r1,g1,b);

% Creating average filter w1=fspecial(‘average’,3);

% applying filter in in separate

% applying average filter

gg=imfilter(g1,w1);

% applying median filter rr=medfilt2(r1);

% Concatenating red,green and blue components to form a composite

image after filtering

image1=cat(3,rr,gg,b);

subplot(131),imshow(a),title(‘original image’);

subplot(132),imshow(image),title(‘noisy image’);

subplot(133),imshow(image1),title(‘filtered image in separate’);

[Type here]

COMP4500

FACULTY OF ENGINEERING – SOHAR

UNIVERSITY

Output:

Homework

1) Take an RGB image (Lab9_3.jpg). and do the following:

1. Insert Gaussian noise on its red component and Salt & pepper noise on its

green and blue components.

2. Concatenating the three components into an image (noisy image).

3. Apply Average Filtering on noisy image from part2.

4. Concatenating the three components into an image (filtered image in separate).

6. Show the results

2)

Repeat the first part by using your own image. Report all the challenges that faced

you in the process of apply noise to an image and then filtering it back.

[Type here]

Electrical &

Computer

Engineering

Program

Lab Exercise

Compu

ter

Vision

and

Image

Process

ing

COMP4500

3

Module 2 Lab Exercise

Introduction to MATLAB Digital Image Processing

1. Lab Objectives:

•

•

Computing of the Fourier Transform for an image and displaying the

spectral image.

Designing of filters in the frequency domain (lowpass and highpass filters)

and apply them to images.

2. Theory

2.1 Fourier Transform:

The Fourier transform is a representation of an image as a sum of complex

exponentials of varying magnitudes, frequencies, and phases. The Fourier

transform plays a critical role in a broad range of image processing applications,

including enhancement, analysis, restoration, and compression.

Working with the Fourier transform on a computer usually involves a form of

the transform known as the discrete Fourier transform (DFT). There are

two principal reasons for using this form:

1) The input and output of the DFT are both discrete, which makes

it convenient for computer manipulations.

2) There is a fast algorithm for computing the DFT known as the fast

Fourier transform (FFT).

The DFT is usually defined for a discrete function f(x,y) that is nonzero only

over the

finite region 0 ≤ x ≤ M-1 and 0 ≤ y ≤ N-1.

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The general idea is that the image (f(x,y) of size M x N) will be represented in the

frequency domain (F(u,v)). The equation for the two-dimensional discrete Fourier

transform (DFT)

The concept behind the Fourier transform is that any waveform that can be

constructed using a sum of sine and cosine waves of different frequencies. The

exponential in the above formula can be expanded into sins and cosines with the

variables u and v determining these frequencies

The inverse of the above discrete Fourier transform is given by the following

equation:

Thus, if we have F(u,v), we can obtain the corresponding image (f(x,y)) using

the inverse discrete Fourier transform.

Things to note about the discrete Fourier transform are the following:

•

The value of the transform at the origin of the frequency domain, at

F(0,0), is called the DC component

•

F(0,0) is equal to MN times the average value of f(x,y) .

•

In MATLAB, F(0,0) is actually F(1,1) because array indices in MATLAB

start at 1 rather than 0.

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•

The values of the Fourier transform are complex, meaning they have

real and imaginary parts. The imaginary parts are represented by i,

which is the square root of -1

•

We visually analyze a Fourier transform by computing a Fourier

spectrum (the magnitude of F(u,v)) and display it as an image. o

The Fourier spectrum is symmetric about the center.

•

The fast Fourier transform (FFT) is a fast algorithm for computing

the discrete Fourier transform.

•

MATLAB has three functions to compute the DFT:

1. fft – for one dimension (useful for audio)

2. fft2 – for two dimensions (useful for images)

3. fftn – for n dimensions

•

MATLAB has three functions that compute the inverse DFT:

1. ifft

2. ifft2

3. ifftn

•

The function fftshift is used to shift the zero-frequency component to center

of spectrum. Note that it is so important to apply a logarithmic

transformation function on the spectral image before displaying so as

spectral details are efficiently displayed.

How does the Discrete Fourier Transform relate to Spatial

Domain Filtering?

The following convolution theorem shows an interesting relationship between

the spatial domain and frequency domain:

And, conversely,

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The symbol “*” indicates convolution of the two functions. The important thing to

extract out of this is that the multiplication of two Fourier transforms corresponds

to the convolution of the associated functions in the spatial domain.

Basic Steps in DFT Filtering

The following summarize the basic steps in DFT Filtering

1. Obtain the Fourier

transform: F=fft2(f);

2. Generate a filter function, H

3. Multiply the transform by the

filter: G=H.*F;

4. Compute the inverse DFT:

g=ifft2(G);

5. Obtain the real part of the inverse FFT of

g: g2=real(g);

2.2 Filters in the Frequency Domain

Based on the property that multiplying the FFT of two functions from the spatial

domain produces the convolution of those functions, you can use Fourier

transforms as a fast convolution on large images. As a note, on small images, it

is faster to work in the spatial domain.

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However, you can also create filters directly in the frequency domain. There are

three commonly discussed filters in the frequency domain:

Lowpass filters, sometimes known as smoothing filters

Highpass filters, sometimes known as sharpening filters

Bandpass filters.

A Lowpass filter attenuates high frequencies and retains low frequencies

unchanged.

A Highpass filter blocks all frequencies smaller than D o and leaves the others

unchanged.

Bandpass filters are a combination of both lowpass and highpass filters. They

attenuate all frequencies smaller than a frequency Do and higher than a

frequency D1, while the frequencies between the two cut-offs remain in the

resulting output image.

Lowpass filters:

create a blurred (or smoothed) image attenuate the high frequencies and leave

the low frequencies of the Fourier transform relatively unchanged

Three main lowpass filters are discussed in Digital Image Processing Using

MATLAB:

1.

Ideal lowpass filter (ILPF): The simplest low pass filter that cutoff all high

frequency components of the Fourier transform that are at the distance greater

than distance D0 from the center.

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Where D0 is a specified nonnegative quantity (cutoff frequency), and D(u,v) is

the distance from point (u,v) to the center of the frequency rectangle. The center

of frequency rectangle is (M/2,N/2)

The distance from any point (u,v) to the center D(u,v) of the Fourier transform is

given by:

M and N are sizes of the image.

2. Butterworth lowpass filter (BLPF): of order n, and with cutoff frequency at

a distance D0 from the center.

3. Gaussian lowpass filter (GLPF)

The GLPF did not achieve as much smoothing as the BLPF of order 2 for the

same value of cutoff frequency

The corresponding formulas and visual representations of these filters are shown

in the table below. In the formulae, D0 is a specified nonnegative number (cutoff

frequency). D(u,v) is the distance from point (u,v) to the center of the filter.

Butterworth low pass filter (BLPF) of order n.

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Highpass filters:

sharpen (or shows the edges of) an image attenuate the low frequencies and

leave the high frequencies of the Fourier transform relatively unchanged

The highpass filter (Hhp) is often represented by its relationship to the lowpass

filter (Hlp):

Because highpass filters can be created in relationship to lowpass filters, the

following table shows the three corresponding highpass filters by their visual

representations:

1. Ideal highpass filter (IHPF)

2. Butterworth highpass filter (BHPF)

3. Gaussian highpass filter (GHPF)

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In Matlab, to get lowpass filter we use this command:

H = fspecial(‘gaussian’,HSIZE,SIGMA)

– Returns a rotationally symmetric Gaussian lowpass filter of size HSIZE with

standard deviation SIGMA (positive).

– HSIZE can be a vector specifying the number of rows and columns in H or

scalar, in which case H is a square matrix.

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– The default HSIZE is [3 3], the default SIGMA is 0.5.

In Matlab, to get highpass laplacian filter we use this command:

H = fspecial(‘laplacian’,ALPHA)

– Returns a 3-by-3 filter approximating the shape of the two-dimensional

Laplacian operator.

– The parameter ALPHA controls the shape of the Laplacian and must be in the

range 0.0 to 1.0.

– The default ALPHA is 0.2

3. Exercises

Exercise1: Apply FFT and IFFT.

%ex1.m close all clear

clc

%====================================

% 1) Displaying the Fourier Spectrum:

%====================================

I=imread(‘Lab8_1.jpg’); I=im2double(I);

FI=fft2(I); %(DFT) get the frequency for the image

FI_S=abs(fftshift(FI));%Shift zero-frequency component

to center of img_spectrum.

I1=ifft2(FI); I2=real(I1);

subplot(131),imshow(I),title(‘Original’),

subplot(132),imagesc(0.5*log(1+FI_S)),title(‘Fourier

Spectrum’),axis off

subplot(133),imshow(I2),title(‘Reconstructed’)

%imagesc: the data is scaled to use the full colormap.

[Type here]

Output:

Exercise2: Apply lowpass filter.

%ex2.m close all clear

clc

%=============================

% 2)

Low-Pass

Gaussian

Filter:

%=============================

I=imread(‘Lab8_1.jpg’); I=im2double(I);

FI=fft2(I);

%1.Obtain the Fourier transform

LP=fspecial(‘gaussian’,[11 11],1.3); %2.Generate a LowPass filter FLP=fft2(LP,size(I,1),size(I,2)); %3. Filter

padding LP_OUT=FLP.*FI; %4.Multiply the transform by the

filter I_OUT_LP=ifft2(LP_OUT); %5.inverse DFT

I_OUT_LP=real(I_OUT_LP); %6.Obtain the real part(Output)

%%%%spectrum%%%%

[Type here]

FLP_S=abs(fftshift(FLP));%Filter spectrum

LP_OUT_S=abs(fftshift(LP_OUT));%output spectrum

subplot(221),imshow(I),title(‘Original’),

subplot(222),imagesc(0.5*log(1+FLP_S)),title(‘LowPass Filter

Spectrum’),axis off

subplot(223),imshow(I_OUT_LP),title(‘LowPass Filtered

Output’)

subplot(224),imagesc(0.5*log(1+LP_OUT_S)),title(‘LowPass

Spectrum’), axis off

Output:

Exercise3: Apply Ideal lowpass filter.

%ex3.m close all clear

clc

a=imread(‘Lab8_2.tif’); [M N]=size(a); a=im2double(a);

F1=fft2(a);

[Type here]

%1.Obtain the Fourier transform

% Set up range of variables. u = 0:(M-1); %0255 v = 0:(N-1);%0-255

% center (u,v) = (M/2,N/2)

% Compute the indices for use in meshgrid

idx = find(u > M/2);% indices 130-255

u(idx) = u(idx) – M;

idy = find(v > N/2);

v(idy) = v(idy) – N;

%set up the meshgrid arrays needed for

% computing the required distances.

[U, V] = meshgrid(u, v);

% Compute the distances D(U, V).

disp(‘IDEAL LOW PASS FILTERING IN FREQUENCY

DOMAIN’); D0=input(‘Enter the cutoff distance==>’);

% Begin filter computations.

H = double(D 30

Homework

1) Write a Matlab code to apply highpass laplacian filter on Lab8_1.jpg image.

2) Write a Matlab code to apply ideal highpass filter on Lab8_1.jpg image for D0=100

3) Apply FFT2, IFFT2, lowpass Gaussian filter, and highpass laplacian filter

onLab8_3.jpg image.

[Type here]

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1 Lab

1 Image

Tags:

Gaussian noise

RGB image

noisy image

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