final_project

MOUNT CARMEL COLLEGE AUTONOMOUS

BENGALURU-560052

DEPARTMENT OF COMPUTER SCIENCE

A PROJECT REPORT ONNOISE REDUCTION IN IMAGES USING FILTERS

SUBMITTED BY

1. PRIYA M

2. DONDLA LEELA VASUNDHARA

3. INDERPREET KAUR

4. NISHA MATHEW

MOUNT CARMEL COLLEGE, AUTONOMOUS

BENGALURU-560052

CERTIFICATION

This is to certify that the project entitled………………………………………has been carried

out by…………………………………………………………in Computer Science under our

supervision and guidance, in partial fulfillment of the requirement for the award of

BACHELOR DEGREE IN SCIENCE for III B.Sc. (VI semester) MOUNT CARMEL

COLLEGE, Autonomous, Bengaluru-52 during the academic year 2015-2016.

Reg. No.:- 1.MS133406 2.MS133407

3. MS133408 4.MS133414

Date of Examination:-

Signature of the Project Guide

Examiner Head of the Department

1)

2)

Noise Reduction In images Using Filters

PREFACE


ABOUT MATLAB

MATLAB (matrix laboratory) is a multi-paradigm numerical computing environment

and fourth-generation programming language. A proprietary programming

language developed by Math Works, MATLAB allows matrix manipulations, plotting

of functions and data, implementation of algorithms, creation of user interfaces, and

interfacing with programs written in other languages, including C, C+

+, Java, Fortran and Python. The MATLAB application is built around the MATLAB

scripting language. Common usage of the MATLAB application involves using the

Command Window as an interactive mathematical shell or executing text files containing

MATLAB code.

MATLAB was first adopted by researchers and practitioners in control engineering, Little's

specialty, but quickly spread to many other domains. It is now also used in education, in

particular the teaching of linear algebra, numerical analysis, and is popular amongst scientists

involved in image processing.


https://en.wikipedia.org/wiki/Image_processing

https://en.wikipedia.org/wiki/Numerical_analysis

https://en.wikipedia.org/wiki/Linear_algebra

https://en.wikipedia.org/wiki/Control_engineering

https://en.wikipedia.org/wiki/Command_line_interface

https://en.wikipedia.org/wiki/Python_(programming_language)

https://en.wikipedia.org/wiki/Fortran

https://en.wikipedia.org/wiki/Java_(programming_language)

https://en.wikipedia.org/wiki/C%2B%2B

https://en.wikipedia.org/wiki/C%2B%2B

https://en.wikipedia.org/wiki/C_(programming_language)

https://en.wikipedia.org/wiki/User_interface

https://en.wikipedia.org/wiki/Algorithm

https://en.wikipedia.org/wiki/Function_(mathematics)

https://en.wikipedia.org/wiki/Matrix_(mathematics)

https://en.wikipedia.org/wiki/MathWorks

https://en.wikipedia.org/wiki/Proprietary_programming_language

https://en.wikipedia.org/wiki/Proprietary_programming_language

https://en.wikipedia.org/wiki/Fourth-generation_programming_language

https://en.wikipedia.org/wiki/Numerical_analysis

https://en.wikipedia.org/wiki/Multi-paradigm_programming_language

ACKNOWLEDGEMENT


“The pleasure that follows the successful completion of an assignment would remain

incomplete without a gratitude for the people and without whose cooperation the achievement

would remain a distant dream. It is not mere formality to place a record the tireless efforts,

ceaseless cooperation, constant guidance and encouragement of the people closely associated

with the assignment but a distinct necessity for facilities the authenticity and readability of

the project.”

The software languages learnt in all these years are brought into practice. We have tried to

make best use of their opportunity. The work hears bears the imprint of many persons under

whom we did this project.

We are thankful to Mrs. Regina, for her scholarly guidance, advice and encouragement and

also for providing necessary facilities to carry out the desertion in prescribed period.

Finally, we are indebted to our family, friends and those people who had helped us in

completing this project.


CONTENTS

TOPICS PAGE NO.

Chapter 1: Introduction 1-2

1.1 : Problem Statement 2

1.2 : Objective 2

Chapter 2: Tools Required 3-4

2.1 : Hardware Requirements 4

2.2 : Software Requirements 4

Chapter 3 : Analysis 5-20

3.1 : Image Processing 6-8

3.2 : Types Of Images 8-11

3.3 : Compression 11

3.4 : Image Representation 11-12

3.5: Spatial Filtering 13

3.6 : Frequency Filtering 13

3.7: Noise 13-15

3.8: Noise Reduction 15-19

3.9 : Performance Parameter 19-20

Chapter 4: Design 21-23

4.1 : Flow Chart 22

4.2 : Program Components 23

Chapter 5: Program Code 24-31

Chapter 6:Snap Shots 32-36

Chapter 7: Challenges & Conclusion 37-38

Chapter 9: Appendix 39-40

Chapter10:Bibliography 41-42


INTRODUCTION

Noise Reduction In images Using Filters1

1.1 PROBLEM STATEMENT

Image noise is random (not present in the object imaged) variation of brightness or color

information in images, and is usually an aspect of electronic noise. Noise is the result of

errors in the image acquisition process that results in pixel values that do not reflect the true

intensities of the real scene. Generally images are degraded with the noise when they are

transmitted or during image acquisition process.

Noise reduction is the process of reducing noise from a digital image. The main purpose of

reducing noise is to enhance the quality of an image. All the noise reduction techniques are

equivalent in a conceptual manner, no matter what image is being processed, however the

approach for implementing these methods may vary with the pre knowledge of the properties

of the images taken into account.

Noises are of different types, and hence it is necessary to provide different techniques

according to the type of noise.

1.2 OBJECTIVE OF THE PROJECT

To study about different types of noise and noise reduction techniques to remove the noise

present in images in digital image processing.


https://en.wikipedia.org/wiki/Electronic_noise

https://en.wikipedia.org/wiki/Image

TOOLS USED


2.1 HARDWARE REQUIREMENTS:

Processor: Intel® Core™2 Duo CPU E7500 @ 2.93GHz 2.93GHz

Installed Memory (RAM): 2.00 GB

System Type: 64-bit Operating System

2.2 SOFTWARE REQUIREMENTS:

Windows edition: Windows 7 professional

Developing environment used: Mat lab 2013


ANALYSIS


3.1 Image Processing

Image processing involves changing the nature of an image in order to eitherto improve its

pictorial information for human interpretation,or render it more suitable for autonomous

machine perception. Digital image processing, which involves using a computer to change

the nature of a digital image. An image can be considered as a two dimensional function,

where the function values give the brightness of the image at any given point. The image

brightness values can be any real numbers in the range 0(black) to 1.0(white). A digital image

differs from a photo in that the x, y and f(x,y)values are all discrete. Usuallythey take on only

integer values, will have x and y ranging from 1 to 256 each, and the brightness values also

ranging from 0 (black) to 255 (white). A digital image, can be considered as a large array of

sampled points from the continuous image, each of which has a particular quantized

brightness; these points are the pixels which constitute the digital image. The pixels

surrounding a given pixel constitute its neighborhood. The procedures which makes an image

look better are:

Enhancing the edges of an image to make it appear sharper.

Removing noise from an image; noise being random errors in the image.

Removing motion blur from an image.

Obtaining the edges of an image. This may be necessary for the measurement of

objects in an image.

3.1.2An image processing task

Acquiring the image.First we need to produce a digital image from a paper envelope.

This can be done using either a CCD camera, or a scanner.

Preprocessing. This is the step taken before the _major_ image processing task. The

problem here is to perform some basic tasks in order to render the resulting image

more suitable for the job to follow. In this case it may involve enhancing the contrast,

removing noise, or identifying regions likely to contain the postcode.


Segmentation. Here is where we actually _get_ the postcode; in other words we

extract from the image that part of it which contains just the postcode.

Representation and description. These terms refer to extracting the particular

features which allow us to differentiate between objects. Here we will be looking for

curves, holes and corners which allow us to distinguish the different digits which

constitute a postcode.

Recognition and interpretation. This means assigning labels to objects based

on their descriptors (from the previous step), and assigning meanings to those labels.

So we identify particular digits, and we interpret a string of four digits at the end of

the address as the postcode.

3.1.3 Aspectsof image processing

Image enhancement: This refers to processing an image so that the result is more suitable forA particular application. Example includes:

Sharpening or de-blurring an out of focus image. Highlighting edges. Improving image contrast, or brightening an image.

Image restoration. This may be considered as reversing the damage done to an image by aKnown cause, for example:

Removing of blur caused by linear motion. Removal of optical distortions. Removing periodic interference.

Image segmentation. This involves subdividing an image into constituent parts, or isolatingCertain aspects of an image:

Finding lines, circles, or particular shapes in an image, In an aerial photograph, identifying cars, trees, buildings, or roads.

3.1.4 Applications

1. Medicine

Inspection and interpretation of images obtained from X-rays, MRI or CAT scans. Analysis of cell images, of chromosome karyotypes.

Noise Reduction In images Using Filters 7

2. Agriculture

Satellite/aerial views of land, for example to determine how much land is being used for different purposes, or to investigate the suitability of different regions for different crops.

Inspection of fruit and vegetables-distinguishing good and fresh produce from old.

3. Industry

Automatic inspection of items on a production line, Inspection of paper samples.

4. Law enforcement

Fingerprint analysis. Sharpening or de-blurring of speed-camera images.

3.2 Types of digital Images

Binary:

Each pixel is just black or white. Since there are only two possible values for each pixel,

We only need one bit per pixel. Such images can therefore be very efficient in terms of

Storage. Images for which a binary representation may be suitable include text (printed or

Handwriting), fingerprints, or architectural plans.

Greyscale:

Each pixel is a shade of grey, normally from 0(black) to 255 (white). This rangemeans that

each pixel can be represented by eight bits, or exactly one byte. This is a verynatural range

for image file handling. Other greyscale ranges are used, but generally they area power of 2.

Such images arise in medicine (X-rays), images of printed works, and indeed 256different

grey levels are sufficient for the recognition of most natural objects.

True color or RGB:

Here each pixel has a particular colour; that colour being described by theamount of red,

green and blue in it. If each of these components has a range 0-255, this givesa total

of different possible colours in the image. This is enough coloursfor any

Noise Reduction In images Using Filters 8

image. Since the total number of bits required for each pixel is 24, such images arealso called

24-bit colour images.

Indexed:

Most colour images only have a small subset of the more than sixteen million possible

Colours. For convenience of storage and file handling, the image has an associated colour

map, or colour palette, which is simply a list of all the colours used in that image. Each pixel

hasa value which does not give its colour (as for an RGB image), but an index to the colour

inthe map.

3.2.2Histograms

Given a greyscale image, its histogram consists of the histogram of its grey levels; that is, a graph indicating the number of times each grey level occurs in the image. We can infer a great deal aboutthe appearance of an image from its histogram, as it indicates

In a dark image, the grey levels (and hence the histogram) would be clustered at the lower end

In a uniformly bright image, the grey levels would be clustered at the upper end: In a well contrasted image, the grey levels would be well spread out over much of the

range.

We can view the histogram of an image in Mat lab by using the imhist function:


>> p=imread('pout.tif');>> imshow(p),figure,imhist(p),axis tight

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3.2.3COLOUR MODEL

A colour model is a method for specifying colours in some standard way. It generally consists

of a three dimensional coordinate system and a subspace of that system in which each colour

is represented by a single point.

RGB:In this model, each colour is represented as three values R, G and Bindicating the amounts of

red,green and blue which make up the colour. This model is used for displays on computer

screens; amonitor has three independent electron “guns”for the red, green and blue

component of each colour.These coloursare not realizable on a computer monitor or TV set,

on which only positive values are possible.The colours corresponding to positive values form

the RGB gamut; in general a colour ”gamut”consists of all the colours realizable with a

particular colour model.

HSV:HSV stands for Hue, Saturation, and Value. These terms have the following meanings:

Hue: The “true colour” attributes (red, green, blue, orange, yellow, and so on).


The image pout. If and its histogram

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Saturation: The amount by which the colour has been diluted with white. The more

white in the colour, the lower the saturation. So a deep red has high saturation, and a

light red (a pinkish colour) has low saturation.

Value: The degree of brightness: a well-lit colour has high intensity; a dark colour has

low intensity.

YIQ:This colour space is used for TV/video in America and other countries where NTSC is the

video standard (Australia uses PAL). In this scheme Y is the ”luminance” (this corresponds

roughly with intensity), and I and Q carry the colour information.

3.3COMPRESSION

The image files can be very large. It is thus important for reasons both of storage and file

transfer to make these file sizes smaller. The different types of compression methods are:

Lossless compression and lossy compression. Lossless compression is preferred for images of

legal, scientific or political significance, whereloss of data, even of apparent insignificance,

could have considerable consequences. Unfortunatelythis style tends not to lead to high

compression ratios. However, lossless compression is used aspart of many standard image

formats. In lossy compression information in the images will be lost.

3.4IMAGE REPRSENTATION

In computing, all data is logically represented in binary. This is true of images as well as

numbers and text. However, an important distinction needs to be made between how image

data is displayed and how it is stored. Displaying involves bitmap representation, whereas

storing as a file involves many image formats, such as jpeg and png. Images data can be

saved or stored in many different formats. The most common are bitmap, jpeg, gif and png.


BitmapA bitmap image has the file extension bmp. Image data is saved to file in an uncompressed

format at a particular bit depth. This is usually 24 bit. This enables millions of colours to be

displayed. Because image data is not compressed when saved to file, file sizes can be very

big. This makes them unsuitable to be used on web pages, even though web browsers will

render them. Bitmaps are the native file format for images made with the MS Paint program.

JpegJpeg stands for Joint Photographic Experts Group. It is a 24 bit format invented specifically

for storing photographs. Being 24 bit millions of colours can be displayed. It is also a lossy

compressed format. This means image data is stored in a compressed form, and some of the

original image data is discarded when it is saved. When saving, a quality setting can be

chosen. Maximum quality is 100. Images will still look good on a web page saved at a quality

of 60. A lower quality setting results in a smaller file size. Jpegs are the predominant format

for images on web pages. Also, most digital cameras save images in this format for reasons of

space efficiency.

GifGif is a lossless format that uses a colour palette of 256 colours (or less). This means it is best

suited for images that do not contain complex colour gradations like photographs do.

Images with blocks of plain colours suit this format. The colours in the colour palette changes

according to the colours in the image, but can never be more than 256 A type of Gif image

format allows for animation.

PngPng or Portable Network graphics is the native format for the Adobe Fireworks program. It

has a lot in common with the Gif format because it can support transparency and is a lossless

saving format. It comes in two versions, 8 bit and 24 bit. The 24 bit version can support many

more colours than Gif - about 16 million. The 8 bit version uses many fewer, so like Gif it is

suitable for plain colours rather than tonal gradations. File sizes compared to jpegs are large

when saving photos in this format, even though compression is an option when saving.


3.5 SPATIAL FILTERING

A spatial filter is an image operation where each pixel value I(u, v) is changed by a function

of the intensities of pixels in a neighborhood of (u, v).The filter is actually a mask of weights

arranged in a rectangular pattern, see next Figure . The process is one of sliding the mask

along the image and performing a multiply and accumulate operation on the pixels covered

by the mask.

3 x 3 Spatial Mask

3.6 FREQUENCY FILETRING

The commonest form of filtering is to remove unwanted frequency components from the data

by band pass frequency filtering. This may be to remove frequencies above the NY Quist

before re-sampling or to remove noise types e.g. low frequency swell noise from the data.

Most commonly band pass filters are applied post-migration to improve the clarity of the

display. While filters can be applied in several domains they are usually designed in

the frequency domain for clarity. They may be applied in the time domain by convolution or

in the frequency domain by multiplication; however this is usually transparent to the user.

The user has to decide whether to apply a minimum phase or zero-phase filter and must input

sufficient parameters to specify the pass or reject bandwidth.

3.7 NOISENoise usually quantified by the percentage of pixels which arecorrupted. Corrupted pixels are

either set to the maximum value or have single bits. There are several ways that noise can be

introduced into an image, depending on how the image has been created. For instance:

• If the image is scanned from a photograph made on film, the film grain is a source of noise.

Noise may also be the result of damage to the film, or be introduced by the scanner itself.


• If the image is acquired directly in a digital format, the mechanism for gathering the data

(such as a CCD detector) can introduce noise.

• Electronic transmission of image data can make noise.

3.7.2Types of Noise

Noise to be any degradation in the image signal caused by external disturbance. If an image is

being sent electronically from one place to another via satellite or wireless transmission or

through networked cables, we may expect errors to occur in the image signal. These errors

will appear on the image output in different ways depending on the type of disturbance in the

signal. Usually we know what type of errors to expect and the type of noise on the image,

hence we investigate some of the standard noise for eliminating or reducing noise in color

image .Image Noise is classified as Amplifier noise (Gaussian noise), Salt-and-pepper noise

(Impulse noise),Speckle noise (Multiplicative noise) and Periodic noise.

1.Gaussian Noise

In Gaussian noise, the value of each pixel gets changes by a small amount from its original

value. A histogram is a graphical representation of the data distribution. It represents the

frequencies shown by the rectangles, having an area equal to the frequency of

observations .The total area of the histogram is equal to the number of observations. The

histogram shows a normal noise distribution. The Gaussian model is a good model because of

the central limit theorem which states that the sum of all different noises represents Gaussian

distribution.

2.Salt and Pepper Noise

The salt and pepper noise is a type of impulse noise. It is a noise which is seen mostly in

images. It contains black pixels on white background and white pixels on black background.

One important feature of this noise is the weight or value of the noisy pixel has no connection

or relation with the color of the neighbor pixels. This type of noise generally affects a little

range of image pixels. When this type of pixel is viewed, it looks like black and white dots.

Sources of this type of noise are dust in the camera andoverheated or defective elements.

There are only twopossible values exists that is a and b and the probability of each is less than

0.2


Reasons for Salt and Pepper Noise:

By memory cell failure.

By malfunctioning of camera’s sensor cells.

By synchronization errors in image digitizing or transmission.

3.Speckle Noise (Multiplicative Noise)

While Gaussian noise can be modeled by random values added to an image, speckle noise

can be modeled by random values multiplied by pixel values hence it is also called

multiplicative noise. Speckle noise is a major problem in some radar applications. It results

from random fluctuations in the return signal from an object that is no bigger than a single

image-processing element.

4.Shot Noise(Poisson Noise)

The dominant noise in the lighter parts of an image from an image sensor is typically that

caused by statistical quantum fluctuations, that is, variation in the number of photons sensed

at a given exposure level; this noise is known as photon shot noise. Shot noise has a root-

mean-square value proportional to the square root of the image intensity, and the noises at

different pixels are independent of one another. Shot noise follows a Poisson distribution,

which is usually not very different from Gaussian. In addition to photon shot noise, there can

be additional shot noise from the dark leakage current in the image sensor; this noise is

otherwise known as "dark shot noise" or "dark-current shot noise".

3.8NOISE REDUCTION

Image noise is an unavoidable side-effect occurring as a result of image capture, more simply

understood as inaudible, yet inevitable fluctuations. In a digital camera, if the light which

enters the lens misaligns with the sensors, it will create image noise. Even if noise is not so

obviously visible in a picture, some kind of image noise is bound to exist. Every type of

electronic device receives and transmits some noise and sends it on to what it is creating.


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When the images are transmitted over channels, they are corrupted with impulse noise due to

noisy channels. This impulse noise consists of large positive and negative spikes. The

positive spikes have values much larger than the background and thus they appear as bright

spots, while the negative spikes have values smaller than the background and they appear as

darker spots. Both the spots for the positive and negative spikes are visible to the human eye.

Also, Gaussian type of noise affects the image. Thus, filters are required for removing noises

before processing. There are lots of filters in the paper to remove noise. They are of many

kinds as linear smoothing filter, median filter, wiener filter and Fuzzy filter.

3.8.2 Linear Filters

Linear filter used to remove certain types of noise. Averaging or Gaussian filters are

appropriate for this purpose. Linear filters also tend to blur sharp edges, destroy lines and

other fine image details, and perform poorly in the presence of signal-dependent noise.

1.Average Filter(Mean Filter)

Average or mean filter is the most simple and the easiest method to implement the smoothing

of images i.e. minimizing the extent of intensity variation among neighboring pixels. It is also

used to minimize noise in images frequently. In this method the filtering concept is to replace

the processing pixel element value in an image matrix with the average value of its neighbors

including itself. Repeatedly it removes those pixel values which are not representing their

surroundings.

Average filter is somewhat equivalent to the convolution filter. This filter also relies on

kernel to represent the shape and size of the vicinity to be sampled while mean is calculated.

3X3 square kernel is frequent in practice ,however in the cases where the demand of high

smoothing comes one can use larger kernels like 5X5, 7X7 etc.Average Filter reads the input

image and sets the image header information for the output image. It applies the smoothing

algorithm and sets the output image as input for the next iteration if it’s not the last iteration

and writes the output image after completion of all the iterations.The mean algorithm works

by adding all the surrounding orneighbor pixel values and takes the mean or average of

thosevalues. The resulting value is placed in the central pixel.


Computing the straightforward convolution of an image with this kernel carries out the mean

filtering process .

5 + 3 + 6 + 2 + 1 + 9 + 8+ 4 + 7 = 45.

45 / 9 = 5.

Center value (previously 1) is replaced by the mean of all ninevalues (5).

2.Adaptive Filter(Wiener Filter)

The wiener function applies a Wiener filter (a type of linear filter) to an image adaptively,

tailoring itself to the local image variance. If the variance is large, wiener performs little

smoothing. If it is small,wiener performs more smoothing. This approach often produces

better results than linear filtering. The adaptive filter is more selective than a comparable

linear filter, preserving edges and other high-frequency parts of an image. In addition, there

are no design tasks; the wiener2 function handles all preliminary computations and

implements the filter for an input image. wiener2, however, does require more computation

time than linear filtering. Wiener works best when the noise is constant-power ("white")

additive noise, such as Gaussian noise. Another method for removing noise is to evolve the

image under a smoothing partial differential equation similar to the heat equation which is

called anisotropic diffusion.

The Fourier domain of the Wiener filter is where :

H*(u, v) = Complex conjugate of degradation function

Pn (u, v) = Power Spectral Density of Noise

Ps (u, v) = Power Spectral Density of non-degraded image

H (u, v) = Degradation function


3.Median Filter

The median filter is normally used to reduce noise in an image, somewhat like the mean

filter. However, median filter is much better than mean filter as it preserves the important

details of the image such as edges. The median filter works like a mean filter only. It

considers each pixel of the image and checks whether that particular pixel is the

representative of its nearby neighbors or not.

Instead of replacing the value of the pixel with the mean of its neighbor pixel values. It

replaces by the median of the neighbor pixel values.

The median is calculated by first sorting all the pixel values from the surrounding

neighborhood into numerical order and then replacing the pixel being considered with the

middle pixel value. If the neighbors of the center pixel are in even number then the mean of

the two middle pixel values are used.

Neighbor values-115,119,120,123,124,125,126,127,150

Average value-124

The median filter is normally used to reduce salt and pepper noise in an image, somewhat

like the mean filter. However, it often does a better job than the mean filter of preserving

useful detail in the image.

4. Geometric Mean FilterIn the geometric mean method, the color value of each pixel is replaced with the geometric

mean of color values of the pixels in a surrounding region. A larger region (filter size) yields

a stronger filter effect with the drawback of some blurring.

The geometric mean is defined as:


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The geometric mean filter is better at removing Gaussian type noise and preserving edge

features than the arithmetic mean filter. The geometric mean filter is very susceptible to

negative outliers.

5.Harmonic Mean FilterIn the harmonic mean method, the color value of each pixel is replaced with the harmonic

mean of color values of the pixels in a surrounding region. The harmonic mean is defined as:

A larger region (filter size) yields a stronger filter effect with the drawback of some blurring.

The harmonic mean filter is better at removing Gaussian type noise and preserving edge

features than the arithmetic mean filter. The harmonic mean filter is very good at removing

positive outliers.

3.9Performance Parameters:

For comparing original image and filtered image, we calculate following parameters:

Mean Square Error (MSE):The MSE is the cumulative square error between the encoded

and the original image defined by:


Where, f is the original image and g is the filtered image. The dimension of the images is m x

n. Thus MSE should be as low as possible for effective filtering.

Signal to Noise Ratiois defined by the power ratio between a signal and the background

noise. WhereP is average power. Both noise and power must be measured at the same points

in a system, and within system with same bandwidth.

Peak signal to Noise ratio (PSNR):

It is defined by:

PSNR = 10 log (255^2/mse)


DESIGN


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4.1 Flow Chart:


Input the Image .

Convert to grey scale Image

Apply noise

Display the image with noise

Histogram of the image with noise

Apply filter

Display the filtered Image

Histogram of Filtered Image

Evaluate the PSNR value

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4.2 Program Component

Load

This feature enables the user to select a picture from the various folders in the

system and once the picture has been selected by the user this feature converts

the selected image into a greyscale image and loads both the original and the

converted image on the workspace in the editor.

Noise

This feature group enables the user to select one noise at a time and displays the

image with noise and its respective histogram.

Filter

This feature group enables the user to select one filter at a time and displays the

filtered image with its histogram.

Close

This feature enables the user to close all the figure windows.


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PROGRAM CODE


function varargout = reduction(varargin)gui_Singleton = 1;gui_State = struct('gui_Name', mfilename, ... 'gui_Singleton', gui_Singleton, ... 'gui_OpeningFcn', @reduction_OpeningFcn, ... 'gui_OutputFcn', @reduction_OutputFcn, ... 'gui_LayoutFcn', [] , ... 'gui_Callback', []);if nargin && ischar(varargin{1}) gui_State.gui_Callback = str2func(varargin{1});end

if nargout [varargout{1:nargout}] = gui_mainfcn(gui_State, varargin{:});else gui_mainfcn(gui_State, varargin{:});end

function reduction_OpeningFcn(hObject, eventdata, handles, varargin)handles.output = hObject;

guidata(hObject, handles);

function varargout = reduction_OutputFcn(hObject, eventdata, handles) varargout{1} = handles.output;

function original_img_Callback(hObject, eventdata, handles)function original_img_CreateFcn(hObject, eventdata, handles)

if ispc && isequal(get(hObject,'BackgroundColor'), get(0,'defaultUicontrolBackgroundColor')) set(hObject,'BackgroundColor','white');end

function browse_Callback(hObject, eventdata, handles)[f,pathname]=uigetfile({'*.*','All Files(*.*)'},'Select your file');original_img=strcat(pathname,f);input=importdata(original_img);axes(handles.disp1);imshow(input);input1=rgb2gray(input);axes(handles.noiseimg);imshow(input1);

save('input1');


function noise_SelectionChangeFcn(hObject, eventdata, handles)

switch get(eventdata.NewValue,'Tag') case 'speckle' load('input1');

noise_img=imnoise(input1,'speckle');

figure,subplot(1,2,1), imshow(noise_img),title('Speckle Noise added to the Image'); subplot(1,2,2),imhist(noise_img),title('Histogram of Noised Image'); save('noise_img')

case 'gaussian' load('input1'); noise_img=imnoise(input1,'gaussian');

figure, subplot(1,2,1), imshow(noise_img),title('Gaussian Noise added to the Image'); subplot(1,2,2),imhist(noise_img),title('Histogram of Noised Image'); save('noise_img') case 'sp' load('input1'); noise_img=imnoise(input1,'salt & pepper'); figure, subplot(1,2,1), imshow(noise_img),title('Salt and Pepper Noise added to the Image'); subplot(1,2,2),imhist(noise_img),title('Histogram of Noised Image'); save('noise_img'); case 'poisson' load('input1'); noise_img=imnoise(input1,'poisson');

figure, subplot(1,2,1), imshow(noise_img),title('Poisson added to the Image'); subplot(1,2,2),imhist(noise_img),title('Histogram of Noised Image');

save('noise_img');

end

function filter_SelectionChangeFcn(hObject, eventdata, handles)switch get(eventdata.NewValue,'Tag') case 'median' load('noise_img');

filtered_img = medfilt2(noise_img,[3 3]); n=size(noise_img); figure,subplot(1,2,1), imshow(filtered_img),title('Noise reduction using Median filter');


subplot(1,2,2),imhist(noise_img),title('Histogram of filtered Image'); save('filtered_img'); case 'wiener' load('noise_img'); filtered_img = wiener2(noise_img,[5 5]); figure, subplot(1,2,1), imshow(filtered_img),title('Noise reduction using Wiener filter'); subplot(1,2,2),imhist(noise_img),title('Histogram of filtered Image'); save('filtered_img'); case 'avg' load('noise_img'); fil=fspecial('average',[3 3]); filtered_img=imfilter(noise_img,fil); figure,subplot(1,2,1), imshow(filtered_img),title('Noise reduction using Average filter'); subplot(1,2,2),imhist(filtered_img),title('Histogram of filtered Image'); save('filtered_img');

case 'gm' load('noise_img'); noise1=im2double(noise_img); [m n]=size(noise1); si=1; for i=1:m for j=1:n con=0; s1=1; for k1=i-si:i+si for p1 = j-si:j+si if((k1 > 0 && p1 > 0) && (k1 < m && p1<n)) con=con+1; s1=s1*noise1(k1,p1); end end end filteredimg(i,j)=s1^(1/con); end end

figure,subplot(1,2,1), imshow(filteredimg),title('Noise reduction using Geometric filter'); subplot(1,2,2),imhist(filteredimg),title('Histogram of filtered Image');

case 'hm' load('noise_img'); noise1=im2double(noise_img); [m n]=size(noise1); si=1; for i=1:m for j=1:n con=0; s1=0;


for k1=i-si:i+si for p1 = j-si:j+si if((k1 > 0 && p1 > 0) && (k1 < m && p1<n)) con=con+1; if noise1(k1,p1)== 0 s1=s1+0; else s1=s1+(1/noise1(k1,p1)); end end end end filteredimg(i,j)=con/s1; end end

figure,subplot(1,2,1), imshow(filteredimg),title('Noise reduction using Harmonic filter'); subplot(1,2,2),imhist(filteredimg),title('Histogram of filtered Image');

case 'adp' load('noise_img');

load('input1'); sz=size(input1,1)*size(input1,2); noise_img1=double(noise_img);

m=5; n=5;

c=padarray(noise_img1,[round(m/2),round(n/2)]);

lvar=zeros([size(noise_img1,1) size(noise_img1,2)]); lmean=zeros([size(noise_img1,1) size(noise_img1,2)]); temp=zeros([size(noise_img1,1) size(noise_img1,2)]); newimg=zeros([size(noise_img1,1) size(noise_img1,2)]);

for i=1:size(c,1)-m-1 for j=1:size(c,2)-n-1 temp=c(i:i+(m-1),j:j+(n-1)); tmp=reshape(temp,[1,(m*n)]); lmean(i,j)=mean(tmp); lvar(i,j)=mean(tmp.^2)-mean(tmp).^2; end end

nvar=sum(lvar(:))/sz; lvar=max(lvar,nvar); newimg=nvar./lvar;


newimg=newimg.*(noise_img1-lmean); filteredimg=noise_img1-newimg; filteredimg=uint8(filteredimg); figure,subplot(1,2,1), imshow(filteredimg),title('Noise reduction using Adaptive filter'); subplot(1,2,2),imhist(filteredimg),title('Histogram of filtered Image');

end

close all;

function varargout = untitled(varargin)

gui_Singleton = 1;gui_State = struct('gui_Name', mfilename, ... 'gui_Singleton', gui_Singleton, ... 'gui_OpeningFcn', @untitled_OpeningFcn, ... 'gui_OutputFcn', @untitled_OutputFcn, ... 'gui_LayoutFcn', [] , ... 'gui_Callback', []);if nargin && ischar(varargin{1}) gui_State.gui_Callback = str2func(varargin{1});end

if nargout [varargout{1:nargout}] = gui_mainfcn(gui_State, varargin{:});else gui_mainfcn(gui_State, varargin{:});endfunction untitled_OpeningFcn(hObject, eventdata, handles, varargin)handles.output = hObject;

guidata(hObject, handles);function varargout = untitled_OutputFcn(hObject, eventdata, handles) varargout{1} = handles.output;

function load1_Callback(hObject, eventdata, handles) [f,pathname]=uigetfile({'*.*','All Files(*.*)'},'Select your file');original_img=strcat(pathname,f);image=importdata(original_img);axes(handles.imgload);imshow(image);image1=rgb2gray(image);axes(handles.imgload1);imshow(image1);

save('image1');


29

m=n(1); q=n(2); MSE=sum(sum((image1-filtered_img).^2))/(m*q); PSNR=10*log10(255*255/MSE); figure,subplot(1,2,1), imshow(filtered_img),title('Noise reduction using Average filter'); subplot(1,2,2),imhist(filtered_img),title('Histogram of filtered Image'); message = sprintf(' The PSNR = %.2f',PSNR); msgbox(message); case 'gm' load('image1'); noise1=im2double(image1); [m n]=size(noise1); si=1; for i=1:m for j=1:n con=0; s1=1; for k1=i-si:i+si for p1 = j-si:j+si if((k1 > 0 && p1 > 0) && (k1 < m && p1<n)) con=con+1; s1=s1*noise1(k1,p1); end end end filteredimg(i,j)=s1^(1/con); end end

figure,subplot(1,2,1), imshow(filteredimg),title('Noise reduction using Geometric filter'); subplot(1,2,2),imhist(filteredimg),title('Histogram of filtered Image'); filteredimg1=im2double(filteredimg); n=size(noise1); m=n(1); q=n(2); MSE=sum(sum((noise1-filteredimg1).^2))/(m*q); PSNR=10*log10(255*255/MSE); message = sprintf(' The PSNR = %.2f',PSNR); msgbox(message);

case 'hm' load('image1'); noise1=im2double(image1); [m n]=size(noise1); si=1;


for i=1:m for j=1:n con=0; s1=0; for k1=i-si:i+si for p1 = j-si:j+si if((k1 > 0 && p1 > 0) && (k1 < m && p1<n)) con=con+1; if noise1(k1,p1)== 0 s1=s1+0; else s1=s1+(1/noise1(k1,p1)); end end end end filtered_img(i,j)=con/s1; end end filtered_img1=im2double(filtered_img); n=size(noise1); m=n(1); q=n(2); MSE=sum(sum((noise1-filtered_img1).^2))/(m*q); PSNR=10*log10(255*255/MSE); figure,subplot(1,2,1), imshow(filtered_img),title('Noise reduction using Harmonic filter'); subplot(1,2,2),imhist(filtered_img),title('Histogram of filtered Image'); message = sprintf(' The PSNR = %.2f',PSNR); msgbox(message);end

function close_Callback(hObject, eventdata, handles)close all;


SNAPSHOTS


CHALLENGESAND

CONCLUSION


Challenges

The project is limited to the default variance value for the different types of noise. It is also

limited to only basic noise removal techniques. The project is not made flexible to enhance an

image after the removal of noise.

Conclusion

In this project, we discussed different filtering techniques for removing noises in image.

Furthermore, we presented and compared results for these filtering techniques.The image is

given as the input, if the input image is colour image then it is converted in to gray scale in

order to reduce the processing time the contrast of input image is enhanced and the image is

denoised using various filter and the quality and accuracy is measured by PSNR and MSE

value and we evaluated the best denoised image according to these value.

This project can also be updated and made a little more accurate with use of other complex

technologies and methodologies and implemented for use of analyzing the satellite imagery.

The various unwanted things that appear in the satellite imagery like the clouds, fog, rain,

etc., can be removed and a clear image with the clear view of the edges of land and other

natural features clearly visible.


APPENDIX


1.Pixel:A minute area of illumination on a display screen, one of many from

which an image is formed.

2.Image Acquisition:It is defined as the action of retrieving an image.

3.Hue:A color or shade.

4.Convolution:It is a small matrix used for blurring ,sharpening, embossing

and more in image processing.

5.Spike:A spike is a sharp rise in the frequency for a given variable.

6.Power Spectral Density:It is a measure of a signal’s power intensity in

the frequency domain.

7.Enhance:Intensify or increase the quality of the image.

8.Sharpening: Enhances the edges in an image.

9. Denoise:Extraction of noise from the image.


BIBLIOGRAPHY


[1] Rafael Gonzalez and Richard E. Woods. Digital Image Processing. Addison-Wesley, second edition, 2002.

[2] Alasdair MacAndrew. An Introduction to Digital Image Processing with Mat lab, 2004.

[3] C. Mythili and V. Kavitha. Efficient Technique for Color Image Noise Reduction. The Research Bulletin of Jordan ACM, Vol. II (III) 41-44.2011.

[4] Milindkumar V. Sarode and Prashant R. Deshmukh. Reduction of Speckle Noise and Image Enhancement of Images

Using Filtering Technique. International Journal of Advancements in Technology. Vol 2, No 1 .January 2011.

[5] K.M. Sharavana Raju,Mohammad Shahnawaz Nasir and T. Meera Devi.Filtering Techniques to reduce Speckle Noise and Image Quality Enhancement methods on Satellite Images.IOSR Journal of Computer Engineering (IOSR-JCE).Volume 15, Issue 4 (Nov. - Dec. 2013),PP 10-15.

[6] Amandeep Kaur and Karamjeet Singh. SPECKLE NOISE REDUCTION BY USING WAVELETS.NCCI 2010 -National Conference on Computational Instrumentation CSIO Chandigarh, INDIA, 19-20 March 2010.

[7] Jyoti Jaybhay and Rajveer Shastri. A STUDY OF SPECKLE NOISE REDUCTION FILTERS. Signal & Image Processing : An International Journal (SIPIJ) Vol.6, No.3, June 2015.

[8] P.Deepa and M.Suganthi. Performance Evaluation of Various Denoising Filters for Medical Image. International Journal of Computer Science and Information Technologies, Vol. 5 (3) , 2014.

[9] Ian T. Young , Jan J. Gerbrands and Lucas J. van Vliet. Fundamentals of Image Processing. Version 2.3 © 1995-2007.

[10] Harsh Prateek Singh,Ayush Nigam,Amit Kumar Gautam,Aakanksha Bhardwaj and Neha Singh.Noise Reduction in Images using Enhanced Average Filter. International Journal of Computer Applications® (IJCA) (0975 – 8887) .

International Conference on Advances in Computer Engineering & Applications (ICACEA-2014) at IMSEC,GZB


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