Spatial Domain Is Direct Computer Science Essay

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In image processing is used in many applications like Gray scale modification, Earth sciences, Remote sensing, finger print identifications etc. An Image is an array matrix, of square of pixels arranged in rows and columns. Pixel is widely used in the term and it is denote the elements of an image. Image enhancement is process of images more useful. It is mainly used to improve the quality of images, removing noise from the images. Histogram equalization is mainly used in the field of image processing and which is used in the form of cumulative distributive function. The main purpose of image processing is to allow the human beings to obtain an image of high quality or descriptive characteristics of original image. The medical images where used in the part of human body .The average image intensity level are 0 to 255. Image enhancement can be spitted into two different types:

Spatial domain

Frequency domain

Spatial domain

Spatial domain is direct manipulation of image pixels. It is a manipulation or smooth and sharpening filtering images.

Frequency Domain

It is used to perform with based purely on convolution theorem and also it is used to change the image position. Image is in the form of frequency domain, the image is computed into Fourier transform.

Image Enhancement

Image enhancement is the process of adjusting the digital images that the result is more suitable for further analysis. You can perform image enhancement in Mat lab with image processing toolbox. Image enhancement provides the following algorithms,

Contrast limited adaptive histogram equalization(CLAHE)

Decor relation stretch

Histogram equalization

Median filtering

Image enhancement where used in many fields like medical, color, picture enhancement.

Contrast Stretching

The contrast stretching increase the dynamic range of gray levels.

Gray-level Slicing

Gray level slicing highlights or suppresses a specific range of gray level in a image

Bit-plane Slicing

Each pixel in an image is represented at 8-bits.

Types of Edge Detection Algorithms:

Sobel edge detection operator

Canny edge detection operator

Prewitt operator

Robert’s operator

Sobel edge detection operator

Sobel edge operator is mainly used to detect the edges in mages. The sobel edge detector calculates the gradient of an image at each point. This operator consists of 3*3 pair of kernels. These kernels can be applied separately to the input image.

-1 0 1 1 2 1

-2 0 2 0 0 0

-1 0 1 -1 -2 -1

Gx Gy

Masks used by sobel operator

Canny edge detection operator

The canny edge detector is most commonly used edge detection algorithm. canny edge detector first smoothes the image to eliminate and noise. First step is to filter out any noise in an original image before trying to detect the original image. Second step is smoothing the image by eliminating the noise. Here it was very successful edge detection operator.

Robert’s operator

The Robert’s operator performs a simple, quick to compute, 2-D spatial measurement on an image. This operator is very similar to the sobel operator.

Prewitt operator

It is similar to the sobel operator and also it is used to detect the horizontal and vertical edges of images.

1.2Project Description

Image Enhancement is to develop ideal histogram equalization is used in the contrast enhancement technique. Histogram equalization is a very popular image enhancement technique. AMBE and Entropy are used to access the histogram equalization techniques. The statistical evaluation result shows that two IQM have poor correlation with Mean Opinion Score (MOS).Compare to the existing system author proposed a new IQM, It will probably give it to the better result. Histogram equalization which is created in the form of Cumulative Distributive Function (CDF). Histogram equalization was used to identify the enhanced image which is used with the help of original image and also the noise is occurred in the image. Texture masking is used to find the edges in the images.

CHATER-2

LITERATURE SURVEY

2.1Image enhancement

Image enhancement is process of making the images more useful and also it is getting a clearer image.

The reasons for doing this include:

Highlighting, interesting detail in images

Removing noise from images

Making images more visually appealing.

2.1.1Spatial domain

Direct manipulation pixel in image pixels. It is a manipulation or changing the image representations and also it is used into many fields such as smooth and sharpening filtering images.

A digital gray level

A digital gray level is a simple two dimensional numbers ranging from 0 to 255. These numbers represent different shades of gray. The number 0 represent pure black color and the number 255 represent pure white color.

Create negative of an image

The most basic and simple operation in digital image processing is to compute the negative of an image.

2.1.2Frequency domain

Transform the image into frequency representation. is used to perform with based purely on convolution theorem and also it is used to change the image position. Image is in the form of frequency domain, the image is computed into Fourier transform.

The Fourier transform

Functions that are NOT periodic BUR with finite area under the curve can be expressed as the integral of sins and/or cosines multiplied by a weight function.

Sampling

Sampling equals to multiplying with a comb filter in the spatial domain.

Image enhancement methods

1. Adaptive Histogram Equalization

HE is not suitable for consumer electronics because it could create most of problems. Root Mean Separation is a brightness preservation technique. The preservation ranging is from 0 to 100%. The Dynamic Range value is changed at the output and also the output is based on the picture quality. Here different images having to produce different results. Frequency should be low when the uniform histogram distribution. It offers low frequency. Computation complexity is significantly reduced. Finally the DRSHE could utilize in consumer electronics like LCD and Plasma Display Panel (PDP) TV.

2. Histogram Equalization

Histogram equalization is broadly used in the field of contrast enhancement. Proposed algorithm mainly focuses on the novel extension and also used to utilize histogram equalization. Ultimate goal is present the brightness value. In this paper newly developed one binary preserved histogram equalization is proposed. Many applications can be made up of the proposed algorithm. Main aim of proposed algorithm is to reduce the complexity.

In this paper is referred to as the generalization of Histogram Equalization. Histogram equalization is not delivered a proper result in such applications. These paper is mainly proposes on brightness preservation techniques. Histogram equalization is significantly introducing the brightness of the image. The consumer electronics field can collect at variety of images is involved. Scalability is the most important property and adjusts the image quality. Ultimate goal of this Histogram Equalization is to allow higher level of brightness preservation. Future work of this paper is to lookout the effective implementation with the use of histogram equalization.

Histogram equalization is a one of the useful technique, proposed method and also the comparison of some histogram equalization methods and enhances the contrast, preserve the image as brightness. Different Histogram equalization methods can be used in the images. Each picture is having their own ratio. Experimental results show that two methods M and D are given the best results.

Propose a new method known as Brightness Preservation technique. This preservation technique can fulfill the requirement of aforementioned problems. To overcome this problem new mean brightness preservation is added. Each input image is carried out by sub histogram. Performance measure is calculated with the use gray scale preserved brightness images. Future work is recommended to introduce the new measure which is also used to evaluate the performance.

3. Decor relation Stretch

Proposes a practical implementation approach of decor relation and linear contrast image enhancement technology in image processing. The main aim is to extend the medical imaging for visual interpretation such as cerebral.

Proposes two pre-processing techniques are implemented. Both two methods are mainly used to improve the classification accuracy. Main aim of this method is to improve the interrupted images and also improve the classification results.

4. Image Adjust

Proposed method is based on extensive experiment. This paper novel extension of aging scheme is extracted and also the automatic age is to be identified. Human age is estimated based on the genes. The face images patches at different intensity level. Future work is recommended to improve the accuracy.

Proposes a new image enhancement method with it is based on the Non-sub Sampled Contour let Transform (NSCT). The proposed algorithm enhances the dynamic range of the image. We have proposed a novel algorithm for multi-scale image enhancement based on the NSCT and also the algorithm can be applied to gray-scale and both color images.

5. Image Noise

Related work of this paper is related to partial differential equation based schemes for image processing may be easily incorporated in our framework.

Film-screen mammography has been the most common and effective technique for the disease for breast cancer. Full-Field Digital Mammography (FFDM) is essential to increase the sensitivity of mammography. In our point of view the proposed methods of this paper is to minimize and avoid alcohol, exercise regularly and also take your supplements daily. Then only you avoid the breast cancer.

This paper is mainly focus on canny edge detector and it is the most popular edge detection technique and also it is the one of the successful edge detector. Future work is recommended to investigate the computing those parameter using property of image such as histogram. New step is also used to increase the computational time. Incorporate peer group and neighbor group consideration can be used to improve edge detection performance.

Proposes a plentiful algorithm is used to improve the quality of poor illumination image. Simulation result is purely based on the proposed algorithm.

Proposed method, the PDF based histogram equalization is performed. Video enhancement application is also presented in the proposed method. Proposed methods belongs to two categories,

1. Adaptive Histogram Equalization (AHE)

2. Improved global method based Histogram Equalization

Proposed weighted threshold enhancement also performed modified histogram. In this weighted threshold also on the luminance component. Tested the proposed weighted threshold method can be performed videos and images and also different HE is proposed. The proposed weighted threshold methods provide good trade-off features. Tested the weighted threshold is suitable for video processing.

Various different methods have been proposed of enhancement. This paper presenting visually low-complexity. Proposed algorithm, require any particular operation. Time-complexity of weight threshold is proposed algorithm. Proposed method is applicable for variety of images and videos. Low-complexity algorithm is suitable for proposed video display application.

Proposed algorithm novel adaptive histogram equalization is used. Proposed method focuses on the face images and face recognition task. The proposed contrast enhancement scheme used in adaptive regions. Compared to existing method it is given illumination in face images. The proposed enhanced method emphasizes each detail in original image.

Novel contrast enhancement and brightness preserve method have been proposed. The proposed method is able to maintain the mean brightness. This method is to perform by producing cleared enhance image with brightness. Proposed method needs less processing and low-complexity.

Proposes a new method not only preserve the brightness of image and also improve the contrast. The local and global histogram equalization method is widely used. In the brightness preserving method, the image is divided into sub-histograms. Performance of the proposed method, were compared to those of the global, brightness preserving an dual sub-image histogram equalization. Each method the absolute value will be changed. Value will based on the image intensity.

Image Enhancement Methods

Adaptive Histogram Equalization

Histogram Equalization

Décor-relation Stretch

Image Adjust

Image Noise

Fig 1 Types of Image Enhancement Methods

CHAPTER-3

PROBLEM STATEMENT AND SOLUTION

3.1EXISTING SYSTEM

Existing system, Absolute Mean Brightness Error (AMBE) and Entropy are among the two most popular Image Quality Measure (IQM) which is used to access the histogram equalization based techniques. Those measures are not giving to poor correlation with Human Visual Perception (HVP). Also this method uses luminance and texture masking images are compared.

3.2PROPOSED SYSTEM

Proposed system, a new image quality is measured. When compared to the existing system it will give it to the better result. The proposed system focused on histogram equalization cumulative density function, histogram equalization table values and masking. Those edge detection methods are producing good result.

CHAPTER-4

STSTEM ANALYSIS

4.1System Specification

4.1.1 Hardware Specification

Processor : Intel Core i3

Clock speed : 2.13 GHz

RAM : 3 GB

Hard disk : 320 GB

4.1.2Software Specification

Operating system : Windows 7 Professional

Programming language: MATLAB 7.0

Mat lab is short for Matrix Laboratory and was originally a tool for performing matrix algebra. It was developed by math works. Mat lab allows matrix multiplication, plotting functions and implementation of algorithm and interfacing with other programming languages including C, C++ and Java. In 2004 had around one million users across industry and academia. Mat lab users come from various backgrounds of engineering, science and economics. Mat lab is widely used in academic and research institutions as well as industry enterprises. Mat lab has in build functions and also vector, classes and variables. Tool boxes which are available including image processing, control systems, fuzzy logic, simulation and many others.

CHAPTER-5

SYSTEM DESIGN

5.1 OVERVIEW OF MODULES

1. Histogram Equalization with Cumulative Distributive Function

This module histogram equalization where applied in cumulative distributive, when the original image is applied to the histogram equalization.

2. Histogram Equalization Table Values

Histogram equalization image will be based on the original image and also the table values will be displayed.

3. Masking

Here the Luminance, Contrast and Texture Masking were implemented.

4. Contrast Sensitivity Function

Contrast sensitivity is the measure of the ability to discern between of different level in static images.

5.2 Flow diagram

Masking

Luminance Masking

Contrast Masking

Texture Masking

Contrast Sensitivity Function

After Histogram Equalization

Before Histogram Equalization

Histogram equalization table values

Histogram equalization with Cumulative Distributive Function

Start

Stop

Fig 2 Flow diagram of Histogram Equalization Methods

CHAPTER-6

IMPLEMENTATION

6.1 DESCRIPTION OF MODELS

1. Histogram equalization with Cumulative Distributive Function

Input image is converted it into the Equalized image with the use of cumulative distributive function. Equalized image is having three different form of image. Those images are not same. Each one is having different result.

Input image

Cumulative Distributive Function

Equalized image

Fig 3 Histogram equalization With CDF

2.Histogram equalization table values

Original image

Equalized image

Table valuesOriginal image is converted to the histogram equalized image, when the histogram equalization table is formed and then graph will be generated.

Fig 4 Table values

3.Masking

Masking is used in the form of three different types

Texture masking

Contrast masking

Luminance masking

Texture masking

Contrast masking

Luminance Masking

Fig 5 Masking

CHAPTER-7

SAMPLE CODING

1.Equalized Histogram

I = imread('peppers.png');

ieqhist = imghisteq(I);

figure; stem(ieqhist); title('Equalized Histogram');

2.Equalization Main

I = imread('peppers.png');

J = imgeqmapping(I);

figure; imagesc(I); colormap('gray'); axis image; title('Input image');

figure; imagesc(J); colormap('gray'); axis image; title('Equalized image');

3.Function color CSF

function func_ColorCSF

freq = logspace(0.2 , 1 , 100)';

C = logspace(-2, 0 , 100);

L = 100;

x = linspace(-pi, pi, 100);

y = linspace(1, 100, 100);

[xx,yy] = meshgrid(x, y);

[newfreq , newC] = meshgrid(freq, C);

z = L .* (newC .* sin(pi .* newfreq .* xx) + 1);

figure, imshow(z, []);

x = linspace(0,1,256);

y = [1 2 3];

[yy, xx] = meshgrid(y, x);

y = zeros(256, 1);

xx(:, 3) = y;

y = xx(:, 2);

xx(:, 2) = y(end : -1 : 1);

colormap(xx);

shading interp;

axis('off');

4.Contrast masking

function func_ContrastMasking

freq = 1;

C = 0.3;

L = 100;

x = linspace(- 1.5 * pi, 0.5 * pi, 100);

y = linspace(1, 100, 100);

[xx,yy] = meshgrid(x, y);

z1 = L .* (C .* sin(2 .* pi .* freq .* xx) + 1);

imwrite(z1, gray(256), 'contrastmasking1.bmp', 'bmp');

figure ,imshow('contrastmasking1.bmp');

d = 0.1;

C = C + d;

z2 = L .* (C .* sin(2 .* pi .* freq .* xx) + 1);

imwrite(z2, gray(256), 'contrastmasking2.bmp', 'bmp');

figure ,imshow('contrastmasking2.bmp');

C = 0.6

z3 = L .* (C .* sin(2 .* pi .* freq .* xx) + 1);

imwrite(z3, gray(256), 'contrastmasking3.bmp', 'bmp');

figure ,imshow('contrastmasking3.bmp');

C = C + d;

z4 = L .* (C .* sin(2 .* pi .* freq .* xx) + 1);

imwrite(z4, gray(256), 'contrastmasking4.bmp', 'bmp');

figure ,imshow('contrastmasking4.bmp');

5.Function Gray CSF

function func_GrayCSF

freq = logspace(0.1, 0.9, 100)';

C = logspace(-2, 0, 100);

L = 100;

x = linspace(-1.5 * pi, 0.5 * pi, 100);

y = linspace(1, 100, 100);

[xx,yy] = meshgrid(x, y);

[newfreq , newC] = meshgrid(freq, C);

z = L .* (newC .* sin(2 .* pi .* newfreq .* xx) + 1);

figure, imshow(z, []);

shading interp;

axis('off')

6.Luminance Masking

function func_LuminanceMasking

freq = 1;

C = 0.05;

L = 100;

x = linspace(-1.5 * pi, 0.5 * pi, 100);

y = linspace(150, 50, 100);

[xx,yy] = meshgrid(x, y);

i = 1;

for L = 100 : 20 : 200;

z = L .* C .* sin(2 .* pi .* freq .* xx) + L;

imagesc(z);

colormap gray;

shading interp;

ch = ['luminancemasking', num2str(i), '.jpg'];

imwrite(z, gray(256), ch, 'jpg');

figure , imshow(ch);

i = i + 1;

end

7. Histogram equalization with cumulative distributive function

I= imread('peppers.png');

ieqhist = imghisteq(I);

figure; stem(ieqhist);

title('Equalized Histogram');

I = imread('peppers.png');

J = imgeqmapping(I);

figure; imagesc(I); colormap('gray'); axis image; title('Input image');

figure; imagesc(J); colormap('gray'); axis image; title('Equalized image');

I = imread('peppers.png');

icdf = imgcdf(I);

figure; stem(icdf);

title('Cumulative Distribution Function (CDF)');

I=imread('peppers.png');

ihist = imghist(I);

figure; stem(ihist);

title('Image Histogram');

I=imread('peppers.png');

icdfnorm = imgnormcdf(I);

figure; stem(icdfnorm);

title('Normalized CDF');

I=imread('peppers.png');

pdfhist = imgpdf(I);

figure; stem(pdfhist);

title('Normalized Histogram (PDF)');

8. Histogram equalization table values

function histtablemain

GIm=imread('peppers.png');

numofpixels=size(GIm,1)*size(GIm,2);

figure,imshow(GIm);

title('Original Image');

HIm=uint8(zeros(size(GIm,1),size(GIm,2)));

freq=zeros(256,1);

probf=zeros(256,1);

probc=zeros(256,1);

cum=zeros(256,1);

output=zeros(256,1);

%freq counts the occurrence of each pixel value.

%The probability of each occurrence is calculated by probf.

for i=1:size(GIm,1)

for j=1:size(GIm,2)

value=GIm(i,j);

freq(value+1)=freq(value+1)+1;

probf(value+1)=freq(value+1)/numofpixels;

end

end

sum=0;

no_bins=255;

%The cumulative distribution probability is calculated.

for i=1:size(probf)

sum=sum+freq(i);

cum(i)=sum;

probc(i)=cum(i)/numofpixels;

output(i)=round(probc(i)*no_bins);

end

for i=1:size(GIm,1)

for j=1:size(GIm,2)

HIm(i,j)=output(GIm(i,j)+1);

end

end

figure,imshow(HIm);

title('Histogram equalization');

%The result is shown in the form of a table

figure('Position',get(0,'screensize'));

dat=cell(256,6);

for i=1:256

dat(i,:)={i,freq(i),probf(i),cum(i),probc(i),output(i)};

end

columnname = {'Bin', 'Histogram', 'Probability', 'Cumulative histogram','CDF','Output'};

columnformat = {'numeric', 'numeric', 'numeric', 'numeric', 'numeric','numeric'};

columneditable = [false false false false false false];

t = uitable('Units','normalized','Position',[0.1 0.1 0.4 0.9], 'Data', dat,'ColumnName', columnname,'ColumnFormat', columnformat,'ColumnEditable', columneditable,'RowName',[]);

GIm1=rgb2gray(GIm);

subplot(2,2,2); bar(GIm1);

title('Before Histogram equalization');

subplot(2,2,4); bar(HIm);title('After Histogram equalization');

9. ICDF

I = imread('peppers.png');

icdf = imgcdf(I);

figure; stem(icdf);

title('Cumulative Distribution Function (CDF)');

10. Image Histogram

I = imread('peppers.png');

ihist = imghist(I);

figure; stem(ihist);

title('Image Histogram');

11. Image CDF

function icdf = imgcdf(img)

if exist('img', 'var') == 0

error('Error: Specify an input image.');

end

icdf = [];

ihist = imghist(img);

maxgval = 255;

icdf = zeros(1,maxgval);

icdf(1)= ihist(1);

for i=2:1:maxgval+1

icdf(i) = ihist(i) + icdf(i-1);

end

end

12.Image Mapping

function ieq = imgeqmapping(img)

ieqhist = imghisteq(img);

[rows,cols] = size(img);

ieq = zeros(rows, cols);

for i=1:1:rows

for j=1:1:cols

pxval = img(i,j)+1;

ieq(i,j) = ieqhist(pxval)-1;

end

end

end

13. Image Histogram

function ihist = imghist(img)

if exist('img', 'var') == 0

error('Error: Specify an input image.');

end

ihist = [];

[rows,cols] = size(img);

maxgval = 255;

ihist = zeros(1,maxgval);

for i=0:maxgval

ihist(i+1) = sum(img(:)==i);

end

end

14. Image histogram equalization

function ieqhist = imghisteq(img)

if exist('img', 'var') == 0

error('Error: Specify an input image.');

end

ieqhist = [];

icdf = imgcdf(img);

[rows,cols] = size(img);

ieqhist = round(255*icdf/(rows*cols));

end

15. Image Normalized Equalization

function icdfnorm = imgnormcdf(img)

if exist('img', 'var') == 0

error('Error: Specify an input image.');

end

icdfnorm = [];

[rows,cols] = size(img);

icdf = imgcdf(img);

icdfnorm = icdf/rows/cols;

end

16. Image PDF

function pdfhist = imgpdf(img)

if exist('img', 'var') == 0

error('Error: Specify an input image.');

end

ihist = imghist(img);

[rows,cols] = size(img);

pdfhist = ihist/rows/cols;

end

17. Normalized CDF

function pdfhist = imgpdf(img)

if exist('img', 'var') == 0

error('Error: Specify an input image.');

end

ihist = imghist(img);

[rows,cols] = size(img);

pdfhist = ihist/rows/cols;

end

18. Normalized histogram equalization

I = imread('peppers.png');

pdfhist = imgpdf(I);

figure; stem(pdfhist);

title('Normalized Histogram (PDF)');

19. Texture Masking

I = imread('peppers.png');

figure, imshow(I);

E = entropyfilt(I);

Eim = mat2gray(E);

imshow(Eim);

BW1 = im2bw(Eim, .8);

imshow(BW1);

figure, imshow(I);

BWao = bwareaopen(BW1,2000);

imshow(BWao);

nhood = true(9);

closeBWao = imclose(BWao,nhood);

imshow(closeBWao)

roughMask = imfill(closeBWao,'holes');

imshow(roughMask);

figure, imshow(I);

I2 = I;

I2(roughMask) = 0;

imshow(I2);

E2 = entropyfilt(I2);

E2im = mat2gray(E2);

imshow(E2im);

BW2 = im2bw(E2im,graythresh(E2im));

imshow(BW2)

figure, imshow(I);

mask2 = bwareaopen(BW2,1000);

imshow(mask2);

texture1 = I;

texture1(~mask2) = 0;

texture2 = I;

texture2(mask2) = 0;

imshow(texture1);

figure, imshow(texture2);

boundary = bwperim(mask2);

segmentResults = I;

segmentResults(boundary) = 255;

imshow(segmentResults);

S = stdfilt(I,nhood);

imshow(mat2gray(S));

R = rangefilt(I,ones(5));

imshow(R);

CHAPTER-8

TESTING

Software testing is any activity aimed at evaluating an attribute or capability of a program or system and determining that it meets its required results. Testing is more than just debugging. The purpose of testing can be quality assurance, verification and validation, or reliability estimation. Testing can be used as a generic metric as well. Correctness testing and reliability testing are two major areas of testing. Software testing is a tradeoff between budget, time and quality.

8.1 UNIT TESTING

Unit testing is software verification and validation method in which tests are conducted to find that the individual units of source code are fit for use. A unit is the smallest testable part of an application. In computer programming, unit testing is a method by which individual units of source code, sets of one or more computer program modules together with associated control data, usage procedures, and operating procedures, are tested to determine if they are fit for use.

In procedural programming a unit could be an entire module but is more commonly an individual function or procedure. In object-oriented programming a unit is often an entire interface, such as a class, but could be an individual method. Unit tests are created by programmers or occasionally by white box testers during the development process.

Test

Case

ID

Test Case

Input

Expected Output

Actual Output

Result

1.

Histogram equalization cumulative distributive function

Input image

Enhanced image

Enhanced image

Pass

Table 1: Test case for Histogram Equalization

Test

Case

ID

Test Case

Input

Expected Output

Actual Output

Result

2.

Histogram equalization with table values

Original image

Table value for histogram

Table value for histogram

Pass

Table 2: Test case for Histogram equalization table values

Test

Case

ID

Test Case

Input

Expected Output

Actual Output

Result

3.

Masking

Texture Masking

Finding an edges

Finding an edges

Pass

Table 3: Test case for Masking

Test

Case

ID

Test Case

Input

Expected Output

Actual Output

Result

4.

Contrast Masking

Gray image

Frequency

Frequency

Pass

Table 4: Test case for Contrast Masking

8.1 INTEGRATION TESTING

Integration testing is a logical extension of unit testing. In its simplest form, two units have already been tested are combined into a component and the interface between them is tested.

Test

Case

ID

Test

Case

Input

Expected Output

Result

1.

Histogram equalization cumulative distributive function

Input image

Enhanced image

Pass

2.

Histogram equalization table values

Original image

Table value of histogram

Pass

3.

Masking

Texture Masking

Finding an edges

Pass

4.

Contrast masking

Gray image

Frequency

Pass

Table 5: Integration testing of improved pans sharpening Method

CHAPTER-9

RESULT AND ANALYSIS

9.1 SCREEN SHOTS

CHAPTER-10

CONCLUSION

Histogram equalization method is used in the form of Cumulative Distributive Function(CDF). Equalized image will based on the input image. This method is used for object identification.Histogram table values will shows the histogram, probability, cumulative histogram and distributive function and output value will be calculated. 1 to 256 bin values can be calculated seperately. Texture masking is used to detect the edges in the images. Contrast Sensitive Function is diaplayed in color and gray image Format. Comapring to other enhancement methods histogram equalization is giving to better result.

CHAPTER-11

FUTURE ENHANCEMENT

In future it will be extended to other image enhancement methods, In histogram equalization is used in other diffferent fields. To detect the edges in images where using edge detection operator was used and also it is used to improved the image quality and measure.

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Exams can be one of the most stressful experiences you’ll ever have! Revision is key, and we’re here to help. With custom created revision notes and exam answers, you’ll never feel underprepared again.