a6 Phase 1 Project

EDGE PRESERVED IMAGE DENOISING

BY USING DIRECTIONAL DIFFERENCE

BASED MEDIAN FILTER

PROJECT REPORT

PHASE I

Submitted by

ARUN.M (080107127013)

JAGANNATHAN.A.J.S (080107127040)

KAJAHUSSAIN.A (080107127043)

AVINASH VIKRAM.M (090407127001)

in partial fulfillment for the award of the degree

of

BACHELOR OF ENGINEERING

in

ELECTRONICS AND COMMUNICATION ENGINEERING

TAMILNADU COLLEGE OF ENGINEERING, COIMBATORE

ANNA UNIVERSITY OF TECHNOLOGY COIMBATORE,

COIMBATORE-641047

OCTOBER 2011

ANNA UNIVERSITY OF TECHNOLOGY COIMBATORE,

COIMBATORE-641047

BONAFIDE CERTIFICATE

Certified that this project report EDGE PRESERVED IMAGE DENOISING BY

USING DIRECTIONAL DIFFERENCE BASED MEDIAN FILTER is the

bonafide work of ARUN.M, JAGANNATHAN.A.J.S, KAJAHUSSAIN.A and

AVINASH VIKRAM.M who carried out the project work under my supervision.

SIGNATURE

Dr.M.KARTHIKEYAN, Ph.D.,

HEAD OF THE DEPARTMENT

Department of ECE,

Tamilnadu College of engineering,

Coimbatore-641659.

SIGNATURE

Mrs.G.SANTHANAMARI, M.E., (Ph.D)

SUPERVISOR

ASSISTANT PROFESSOR

Department of ECE,

Tamilnadu College of engineering,

Coimbatore-641659.

Submitted for the Anna University Examination held on ..

INTERNAL EXAMINER EXTERNAL EXAMINER

ii

ABSTRACT

Digital images are often corrupted by many types of noise

including salt-and-pepper, which are normally affect the acquisition and

transmission. Impulse noise which is a set of isolated pixels can make a great

difference on the configuration of image. It is often caused by malfunctioning

pixels in camera sensors, faulty memory locations in hardware or transmission

of the image in a noisy channel. It is essential to eliminate salt-and-pepper noise

in the image and to preserve the image edge and integrity. The proposed method

consists of a detection stage where the noisy pixels are detected and followed by

filtering which replaces only the noisy pixels. The noisy pixels are detected by

taking the minimum and maximum gray level intensity values in the sliding

window. Then these detected noisy pixels will be restored by the median value

of the noise free pixels in the sliding window, and then the directional

difference median filtering is used to handle the conflict of noise suppression

and edge-preserving. The proposed method is convenient and efficient on

removing salt and pepper noise in the image and at the same time can achieve

good performance of edge preserving, even when the image is corrupted by salt

and pepper noise of higher density. Simulation results shows that it can remove

salt and pepper noise effectively with edge-preserving compared to other

existing nonlinear filters. Thus the proposed method is simple to realize and it is

simulated by using MATLAB.

iii

TABLE OF CONTENTS

CHAPTER NO. TITLE PAGE NO.

ABSTRACT ii

LIST OF FIGURES vi

LIST OF TABLES vii

LIST OF ABBREVIATIONS viii

1 INTRODUCTION 1

1.1 Need For The Project 1

1.2 Motivation 3

1.3 Original Contributions Of The Thesis 4

1.4 Organization Of The Project 5

2 SYSTEM ANALYSIS 6

2.1 Existing System 6

2.1.1. Drawbacks 6

2.2 Proposed System 7

2.3 Feasibility Study 7

2.3.1 Economical Feasibility 7

2.3.2 Operational Feasibility 8

2.3.3 Technical Feasibility 8

iv

3

SYSTEM SPECIFICATION

9

3.1.Hardware Requirements 9

3.2.Software Requirements 9

4 SOFTWARE DESCRIPTION 10

4.1 Matlab Description 10

4.2 Features 11

4.3 Image Processing Tool Box 12

4.3.1 Features Of Image Processing

Tool box 12

4.3.1.1Pre-processing and Post-processing Images 13

4.3.1.2 Image Analysis 13

4.3.1.3 Image Enhancement 14

4.3.1.4 Image Deblurring 15

5 PROJECT DESCRIPTION 17

5.1 Problem Definition 17

5.2 Overview Of The Project 17

5.3 Description Of The Algorithm 18

5.3.1 Noise Detection Algorithm Along with

Adaptive Median Filtering

18

v

5.3.2 Directional Difference Filtering Algorithm 19

5.4 Data Flow Diagram 21

5.5 Data Base Design 22

6 RESULTS AND DISCUSSION 24

6.1 Simulation Results 24

6.2 Performance Analysis

6.2.1comparison Table For 512x512 Lena Image

6.2.2Comparison Table for Peppers Image

25

25

27

7 CONCLUSION 31

7.1 Future Enhancements 31

8 REFERENCES 32

vi

LIST OF FIGURES

FIGURE

NO. TITLE PAGE NO.

4.1 Performing Connected Components Analysis 15

4.2 Image Of The Sun Using Deblurring Algorithms 16

5.1 Fundamental Steps In Digital Image Processing 21

5.2 Standard Testing Images Used In Existing Algorithms 22

5.3 Standard Testing Images Used In Proposed Algorithm 23

6.1

6.2

6.3

6.4

Simulation Results For 60% Corrupted Lena Image

Simulation Results For 60% Corrupted 'Peppers Image

Simulation Results For 60% Corrupted 'Cameraman

Image

Simulation Results For 60% Corrupted 'Rahman Image

26

28

29

30

vii

TABLE NO.

LIST OF TABLES

TITLE

PAGE NO.

6.1

Comparison Of PSNR Values Of Proposed Technique h

With Existing Technique For 512x512 Lena Image. 25

6.2

Comparison Of MSE Values Of Proposed Technique

With Existing Technique For 512X512 LENA IMAGE 25

6.3

Comparison Of PSNR Values Of Proposed Technique

With Existing Technique For 384x512 Peppers Image 27

6.4

Comparison Of MSE Values Of Proposed Technique

With Existing Technique For 384x512 Lena Image

27

viii

ACWM

LIST OF ABBREVIATIONS

Adaptive impulse detector with Center- Weighted Mean

ATMA Alpha-Trimmed Mean-based Approach

BDND Boundary Discriminative Noise Detection

BLAS Basic Linear Algebra Subprograms

CWM Center Weighted median Filter

DBA Decision Based Algorithm

DCT Discrete Cosine Transform

DICOM Digital Imaging & Communication in Medicine

DWMF Directional Weighted Median Filter

EDRIN

Efficient Denoising Chip For The Removal Of Impulse

Noise

EEPA Efficient Edge-Preserving Approach

EISPACK Eigen vector Solution Package

FFT Fast Fourier Transform

GUI Graphical User Interface

ICC International Color Consortium

LINPACK Linear algebra Package

MSE Mean Square Error

OCS Open Close Sequence

PSNR

RDRIN

Peak Signal to Noise Ratio

Robust Detection technique for removing

Random valued Impulse Noise

SAWM Switching based Adapted Weighted Mean

SMF Switching Median Filter

CHAPTER 1

1

INTRODUCTION

1.1 NEED FOR THE PROJECT

Image sequences have conquered their place among the most important

information carriers in today's world. Their applications such as broadcasting,

video-phone, traffic observations, surveillance systems, autonomous navigation

and so on. The field of digital image processing refers to processing digital

image by means of a digital computer. It encompasses processes that extract

attributes from images including the recognition of individual objects.

Digital images are often corrupted by many types of noise including

salt-and-pepper noise, which are normally acquired during image acquisition

and transmission. Impulse noise which is a set of isolated pixels can make a

great difference on the configuration of image. It is essential to eliminate salt-

and-pepper noise in the image and preserve the image edge and integrity.

Normally the pixels which have maximum and minimum gray level intensity

value in the sliding window are the pixels that are corrupted by the salt and

pepper noise.

The proposed method concentrates on image denoising where impulse

noises in images are removed. Impulse noise is found in situations where quick

transients such as faulty switching, during imaging, due to transmission errors,

malfunctioning pixel elements in the camera sensors, faulty memory locations,

and timing errors in analog-to-digital conversion. An important characteristic of

this type of noise is that only part of the pixels is corrupted and the rest are

noise-free.

This paper deals with the image restoration which is a highly

developing and gaining importance, because of the significant increase in the

use of digital images over the internet and in enhancement of the images in

various fields of research like.

2

Medical applications and Defence applications

Science and engineering fields.

The image processing is considered to be very important in these for

the pre-processing and the post-processing processes.

3

1.2 MOTIVATION

The acquisition or transmission of digital images through sensors or

communication channels is often interfered by impulse noise. Noise removal

from a corrupted image is finding vital application in image transmission over

the wideband network. It is imperative and even indispensable, to remove these

corrupted pixels to facilitate subsequent image processing operations, such as

edge detection, image segmentation and object recognition.

In digital image processing, noise reduction is one of basic pre-

processing steps usually include image enhancement methods. The aim of

impulse noise reduction is to suppress the noise while preserving the important

fine details and edges which are the two different boundary regions. The

performance of some tasks in next levels such as segmentation/classification for

various applications such that biometric recognition depends on the success of

noise reduction in previous level.

The purpose of image restoration is to "compensate for" or "undo"

defects which degrade an image. Degradation comes in many forms such as

motion blur, noise and camera misfocus. In cases like motion blur, it is possible

to come up with a very good estimate of the actual blurring function and "undo"

the blur to restore the original image. In cases where the image is corrupted by

noise, the best we may hope to do is to compensate for the degradation it

caused. In this project, a new algorithm for effective restoration of degraded

images is proposed.

4

1.3 ORIGINAL CONTRIBUTIONS OF THE THESIS

The main parameters to be considered during restoration of a distorted

image are as follows:

Miss detection rate: The rate at which the noisy pixels in the corrupted

image are missed during the process of detection of noisy pixels.

False alarm rate: The rate at which the noise free pixels are indicated

wrongly as noisy pixels.

Peak Signal to Noise Ratio (PSNR): The phrase Peak Signal-to-Noise

Ratio, often abbreviated PSNR, is an engineering term for the ratio

between the numbers of noise free pixels to the number of noisy pixels of

a corrupted image.

PSNR=

Noise density: The amount of noise added to the original image.

Processing time: The time required for the complete execution of the

program.

MSE: Mean Square Error is defined as the average of the square of the

difference between the desired response and the actual system output(the

error)

MAE: Mean Absolute Error is a quantity used to measure how closed

forecasts or predictions are to the eventual outcomes.

MAE=

5

SSIM: Structural SIMilarity (SIMM) index is a method for measuring the

similarity between two images.

The contributions of this thesis can be generalized as follows:

The proposed algorithm has high ability in identifying impulse noise

thereby achieving zero miss detection rate and false alarm rate.

This algorithm provides high PSNR and low MSE values.

The processing time for this algorithm is very less.

1.4 ORIGANIZATION OF THE PROJECT

The thesis is composed of eight chapters. The structure is as follows:

In Chapter 1, the introduction and need for the project are given.

Chapter 2 describes existing systems with its drawbacks and the proposed

system are analysed with its feasibility study.

Chapter 3 describes the system specifications for both hardware and

software requirements.

Chapter 4 describes the MATLAB software in basic applications of

image processing and numerical calculations along with the Image Processing

Toolbar and its features.

Chapter 5 deals with the description, problem definition and overview of

the project.

In Chapter 6, simulation results and the performance analysis

comparisons between the existing and proposed algorithm are discussed.

Chapter 7 describes the conclusion and future enhancement of the project

is discussed.

In Chapter 8, references are given

CHAPTER 2

6

SYSTEM ANALYSIS

2.1 EXISTING SYSTEM

The median filter is the basic nonlinear filter for removing impulse

noise. It has good noise suppression ability and high computational efficiency

but it is prone to damage such important details as thin lines and sharp corners

since it replaces every pixel by the median value of its neighbouring pixels.

The recent methods which are effective in the removal of random valued

impulse noise are a new Directional Weighted Median Filter (DWMF) for

removal of random valued impulse noise [2], an Efficient Denoising chip for the

Removal of Impulse Noise (EDRIN) [14] And Robust Detection technique for

removing Random valued Impulse Noise (RDRIN) [10]. The DWMF uses

directional weighted filters which are effective in removing very low noise

densities and is not efficient for higher noise densities.

.2.1.1 Drawbacks

The principle drawback is that they have limited performance in terms

of false alarm and miss detections. Hence, they cannot preserve the image

details and edges, especially when the noise is high.

Median filter and other versions of median filters have a good ability,

in the low noise ratios and on the other hands in case of the high noise ratio,

these methods causes blurring image and actually destroys vital textures. In

these cases, the relationship between noise reduction and preservation of

important details of image is very important and needs proper algorithms for

noise reduction. The major drawback of the conventional vector median

approaches is that they apply median operation to each pixel, irrespective of it

being corrupted or not.

7

This approach is quite successful in handling the signal dependent

noise. Impulse noise can also be removed using higher order statistics. But this

method involves computation of higher order statistical terms, which are

computationally expensive [2]. The EDPA, EEPA and ACWN methods only

perform well when an image is corrupted with 50% of salt and pepper noise or

lower [8].

2.2 PROPOSED SYSTEM

In the proposed method there is one stage of detection and two stage of

filtering. In the first stage, the maximum and minimum gray level intensities in

the sliding window of the noisy image are considered as the threshold value to

detect the presence of noise. In first stage of filtering, these detected noisy

pixels are replaced by the median value of the noise free pixels. In second stage

of filtering directional difference median filtering is used for noise suppression

and edge-preservation. The proposed method is convenient and efficient on

removing salt and pepper noise in the image, and at the same time can achieve

good performance of edge preserving, even when the image is corrupted by salt

and pepper noise of high density.

2.3 FEASIBILITY STUDY

A feasibility study is an evaluation of a proposal designed to determine

the difficulty in carrying out a designated task. Generally, a feasibility study

precedes technical development and project implementation. In other words, a

feasibility study is an evaluation or analysis of the potential impact of a

proposed project.

Feasibility analysis carried out for this project is as follows,

2.3.1 Economical feasibility

For any system if the expected benefits equal or exceed the expected

costs, the system can be judged to be economically feasible. In economic

8

feasibility, cost benefit analysis is done in which expected costs and benefits are

evaluated. Economic analysis is used for evaluating the effectiveness of the

proposed system. In economic feasibility, the most important is cost benefit

analysis. An impulse noise detection algorithm for switching median filters is

computationally expensive, but it is simulated and seems to be an exceedingly

effective and accurate algorithm for impulse noise detection methods and this

proposed scheme provides better image restoration comparison with the other

existing conventional methods.

2.3.2 Operational feasibility

Operational feasibility is a measure of how well a proposed system

solves the problems and takes advantage of the opportunities identified during

scope definition and how it satisfies the requirements identified in the

requirements analysis phase of system development.

The highly effective directional median filter behaviour is simpler and

it is easy to implement and preserve image details by selecting only noise

pixels for processing and will speed up the filtering process. They can achieve

good edge preserving performance employing fuzzy sets. With the directional

difference median filter it is useful and effective to removal salt and pepper

noise in image processing compared to the conventional median based filters.

The proposed method provides amazing accurate results even when noise

density is as high as 60%.

2.3.3 Technical feasibility

This project is developed using MATLAB. It is executed in the

minimum hardware of Intel Pentium III,RAM of 1 GB, hard disk capacity of

80GB,15 inches monitor,104 keys and mouse, the software MATLAB, the

windows XP or Linux operating systems and that has been used in this project

are found to be technically feasible.

CHAPTER 3

9

SYSTEM SPECIFICATION

3.1 HARDWARE REQUIREMENTS

PROCESSOR : Pentium III (or higher)

SPEED : 2.53 GHz (or higher)

RAM : 1 GB (or higher)

HARD DISK DRIVE : 40 GB (or higher)

MONITOR : 15 Color (or higher)

KEYBOARD : Logitech 104 Keys (or

Higher)

3.2 SOFTWARE REQUIREMENTS

OPERATING SYSTEM : Windows xp3 (or higher) or

Linux (Red hat or Ubuntu)

DEVELOPMENT TOOL : MATLAB 7.0 (or higher)

CHAPTER 4

10

SOFTWARE DESCRIPTION

4.1 MAT LAB DESCRIPTION

MATLAB is a high-level technical computing language and interactive

environment for algorithm development, data visualization, data analysis and

numeric computation. Using the MATLAB product, we can solve technical

computing problems faster than with traditional programming languages, such

as C, C++ and FORTRAN. MATLAB is an integrated technical computing

environment that combines numeric computation, advanced graphics and

visualization and a high-level programming language.

The MATLAB can be used in a wide range of applications, including

signal and image processing, communications, control design, test and

measurement, financial modeling and analysis and computational biology.

Add-on toolboxes (collections of special-purpose MATLAB functions,

available separately) extend the MATLAB environment to solve particular

classes of problems in these application areas.

MATLAB provides a number of features for documenting and sharing

our work. We can integrate our MATLAB code with other languages and

applications and distribute our MATLAB algorithms and applications.

MATLAB is an interactive system whose basic data element is an array

that does not require dimensioning. It allows us to solve many technical

computing problems, especially those with matrix and vector formulations, in a

fraction of the time it would take to write a program in a scalar non-interactive

language such as C or FORTRAN.

The name MATLAB stands for matrix laboratory. MATLAB was

originally written to provide easy access to matrix software developed by the

11

LINPACK and EISPACK projects. Today, MATLAB engines incorporate the

LAPACK and BLAS libraries, embedding the state of the art in software for

matrix computation.

The MATLAB language is a high-level matrix/array language with

control flow statements, functions, and data structures, input/output and object-

oriented programming features. It allows both "programming in the small" to

rapidly create quick programs we do not intend to reuse. We can also do

"programming in the large" to create complex application programs intended for

reuse.

4.2 FEATURES

The various features of MATLAB are as following

Development environment for managing code, files and data.

Interactive tools for iterative exploration, design and problem solving.

Mathematical functions for linear algebra, statistics, Fourier analysis,

filtering, optimization and numerical integration.

2-D and 3-D graphics functions for visualizing data.

Tools for building custom graphical user interfaces.

Functions for integrating MATLAB based algorithms with external

applications and languages, such as C, C++, FORTRAN, Java, COM

and Microsoft Excel.

Packages MATLAB applications as executable and shared libraries.

Allows distribution of standalone executable and software

components royalty-free.

Allows incorporation MATLAB based algorithms into applications

developed using other languages and technologies.

Encrypts MATLAB code so that it cannot be viewed or modified.

12

4.3 IMAGE PROCESSING TOOLBOX

The image processing tool box is normally used to perform image

processing, analysis and algorithm development. Image Processing Toolbox

software provides a comprehensive set of reference-standard algorithms and

graphical tools for image processing, analysis, visualization and algorithm

development. We can restore noisy or degraded images, enhance images for

improved intelligibility, extract features, analyze shapes and textures and

register two images. Most toolbox functions are written in the open MATLAB

language, giving us the ability to inspect the algorithms, modify the source code

and create our own custom functions.

We can perform image enhancement, image deblurring, feature

detection, noise reduction, image segmentation, spatial transformations and

image registration. Many functions in the toolbox are multithreaded to take

advantage of multicore and multiprocessor computers. Image Processing

Toolbox supports a diverse set of image types, including high dynamic range,

giga pixel resolution, ICC-compliant colour and tomography images. Graphical

tools let we explore an image, examine a region of pixels, adjust the contrast,

create contours or histograms and manipulate regions of interest (ROIs). With

the toolbox algorithms we can restore degraded images, detect and measure

features, analyze shapes and Textures and adjust the colour balance of images.

4.3.1 FEATURES OF IMAGE PROCESSING TOOLBOX

Image enhancement, filtering and deblurring.

Image analysis, including segmentation, morphology, feature extraction

and measurement.

Spatial transformations and image registration.

Image transforms, including FFT, DCT, Radon and fan-beam projection.

13

Workflows for processing, displaying and navigating arbitrarily large

images.

Modular interactive tools, including ROI selections, histograms and

distance measurements.

ICC colour management.

Multidimensional image processing.

Image-sequence and video display.

DICOM import and export.

4.3.1.1 Pre-processing and Post-processing images

Image Processing Toolbox provides reference-standard algorithms for

pre-processing and post processing tasks that solve frequent system problems,

such as interfering noise, low dynamic range, out-of-focus optics and the

difference in colour representation between input and output devices.

4.3.1.2 Image analysis

Image Processing Toolbox provides a comprehensive suite of reference

standard algorithms and graphical tools for image analysis tasks such as

statistical analysis, feature extraction and property measurement.

Statistical functions let you analyze the general characteristics of an

image by:

Computing the mean or standard deviation.

Determining the intensity values along a line segment.

Displaying an image histogram.

Plotting a profile of intensity values.

Edge-detection algorithms let you identify object boundaries in an

image. These algorithms include the Sobel, Prewitt, Roberts, Canny and

14

Laplacian of Gaussian methods. The powerful canny method can detect true

weak edges without being "fooled" by noise.

Image segmentation algorithms determine region boundaries in an

image. We can explore many different approaches to image segmentation,

including automatic thresholding, edge-based methods, and morphology-based

methods such as the watershed transform, often used to segment touching

objects.

Morphological operators enable you to detect edges, enhance contrast,

remove noise, segment an image into regions, thin regions or perform

skeletonization on regions. Morphological functions in Image Processing

Toolbox include:

Erosion and dilation.

Opening and closing.

Labelling of connected components.

Watershed segmentation.

Reconstruction.

Distance transform.

4.3.1.3 Image enhancement

Image enhancement techniques in Image Processing Toolbox enable us

to increase the signal-to-noise ratio and accentuate image features by modifying

the colours or intensities of an image. The following processes can be done:

Perform decorrelation stretching.

Perform histogram equalization.

Remap the dynamic range.

Perform linear, median or adaptive filtering.

15

Figure.4.1. Performing connected components analysis on an image

with non-uniform background intensity using MATLAB and image processing

toolbox.

The toolbox includes specialized filtering routines and a generalized

multidimensional filtering function that handles integer image types, offers

multiple boundary-padding options and performs convolution and correlation.

Predefined filters and functions for designing and implementing our own linear

filters are also provided.

4.3.1.4 Image Deblurring

Image deblurring algorithms in Image Processing Toolbox include

blind, Lucy-Richardson, Wiener and regularized filter deconvolution, as well as

conversions between point spread and optical transfer functions. These

functions help correct blurring caused by out-of-focus optics, movement by the

16

camera or the subject during image capture, atmospheric conditions, short

exposure time and other factors. All deblurring functions work with

multidimensional images.

Figure.4.2. Image of the sun using deblurring algorithms.

CHAPTER 5

17

PROJECT DESCRIPTION

5.1 PROBLEM DEFINITION

In the process of image acquisition and transmission, impulse noises

often cause serious degradation of the image quality. Among the various

filtering algorithms that have been proposed, the family of median filters is the

most popular and holds a dominant position in this area for its simplicity. The

most representative paradigm in this family is known as Switching Median

Filtering (SMF), which partitions the whole filtering process into two

sequential steps - noise detection and filtering. By utilizing the priority

knowledge obtained from the noise detection step, the filtering step could be

more targeted and does not need to touch those uncorrupted pixels. Obviously

the accuracy of the noise detection is critical to the final result. In terms of the

conflict of noise suppression and edge-preserving, our proposed method takes

directional difference method to handle it.

5.2 OVERVIEW OF THE PROJECT

As in image enhancement, the future ultimate goal of restoration

techniques is to improve an image in some predefined sense.

Restoration attempts to reconstruct or recover an image that has been

degraded by using a priori knowledge of the degradation phenomenon. Thus

restoration techniques are oriented toward modelling the degradation and

applying the inverse process in order to recover the original image.

In the proposed method corrupted pixels are detected by using the

minimum and maximum gray level intensity values in the sliding window and

the detected noisy pixels replaced by the median values of noise free pixels and

the directional difference filter is used for edge preserving and effective noise

suppression.

18

The intensity of the noisy pixel will be distinct from its nearest

surrounding pixels. Based on this criterion the proposed method focuses on

noisy pixel detection.

In the first stage, select the window size as 3x3, then the maximum and

minimum gray level values in the window are to identify the noisy pixels. In

next stage, these detected noisy pixels will be replaced by the median value of

the noise free pixels.

In last stage the corrupted pixels which are left unchanged in the

previous stage are restored by using directional difference filters, it also

provides better noise suppression and edge preservation. The proposed method

works well for highly corrupted images with noise densities as high as 60 %

with better PSNR value and improved visual quality.

5.3 DESCRIPTION OF THE ALGORITHM

The description of the algorithm has two stages, they are

Noise Detection Algorithm Along with Adaptive Median Filtering

Directional Difference Filtering Algorithm

5.3.1Noise Detection Algorithm Along With Adaptive Median Filtering

Step 1:

Initialize the window size w =3.

Step 2:

Compute the maximum, minimum and median value in window, they

can be explained as respectively.

19

Step 3:

If , then go to step 5. Otherwise, set N=N+1.

Step 4:

If go to step 2. Otherwise, we replace by when the

window size is .

Step 5:

If , then is noise free, else we replace by

5.3.2 Directional Difference Filtering Algorithm

Step 1:

In this step, the absolute difference of the central pixel from the four

directional pixels in a 3 x 3 window is taken and stored in four sets. The set

giving the directional differences is given as

The four directions are

Step 2:

Where (0, 0) is the central pixel position. Then the sum of the four

directional differences are found as

20

Step 3:

Minimum among the sum are found and the median of the minimum

directional difference is found. Only noise free pixels and pre-processed pixels

in the concerned direction are considered for taking median.

) k=1, 2, 3, 4

Thus the noisy pixels are replaced by the median value of the minimum

of the sum of four directional differences.

21

5.4 DATA FLOW DIAGRAM

Figure.5.1. Fundamental steps in digital image processing

22

5.5 DATABASE DESIGN

Standard testing images used in digital image processing are as follows

Figure.5.2. Standard testing images used in existing algorithms

(a) Lena, (b) Baboon, (c) Bridge, (d) Peppers.

23

(a) (b)

(b) (d)

Figure.5.3. Standard testing images used in proposed algorithm.

(a) Cameraman, (b) M83, (c) Eight, (d) Saturn.

CHAPTER 6

24

RESULTS AND DISCUSSION

6.1 SIMULATION RESULTS

The threshold values are optimized to get better restoration. The

proposed method restores highly corrupted images with noise densities as high

as 60% with improved PSNR values. The tuning parameters are optimized to

yield good result for all noise densities. Hence the fixed values of the tuning

parameters give consistent PSNR values and also MSE value is much reduced.

The proposed method is compared with the existing methods. Three

different test images are used and the corresponding PSNR values and the MSE

values are compared. It can be seen that in the proposed method the PSNR

values are consistent and gives good PSNR for highly corrupted noise with

better image quality. The noise mask is used for processing only the noisy pixels

whereas retaining the noise free pixels. Pixel by pixel processing enables better

image restoration and visual quality.

The Mean Square Error Value (MSE) of the proposed method is

comparatively less to the previous methods. It implies that the false and missed

detections are less increasing the percentage of right detection. The rate of

increase in wrong detection is less than the existing methods and this helps in

better restoration of images that are highly corrupted. Thus the results for

different methods and the proposed method for 60% corrupted Lena image of

512x512 images is shown below.

25

6.2 PERFORMANCE ANALYSIS

6.2.1 Comparison Table for 512x512 Lena Image

Noise Density

in %

DWMF EDRIN RDRIN Proposed Method

30 18.1087 25.5235 26.5900 30.1799

40 16.1872 22.4067 23.3969 29.5786

50 14.7031 19.8100 20.5384 28.7990

60 13.3538 17.4319 18.0601 27.6059

70 12.3060 15.4260 15.7887 25.9309

Table.6.1 Comparison of PSNR values of proposed technique with

existing techniques for 512x512 Lena image

Table.6.2. Comparison of MSE values of proposed technique with

existing techniques for 512x512 Lena image

Noise

Density in

%

DWMF EDRIN RDRIN Proposed

Method

30 1005.1 182.2764 142.5855 62.3867

40 1564.4 373.6041 297.4327 71.6505

50 2201.8 679.3297 574.4358 85.7386

60 3004 1174.6 1016.4 112.8477

70 3823.7 1864.1 1714.8 165.9563

26

Figure.6.1. Simulation results for 60% corrupted Lena image (a)

original image (b) noisy image (c) DWMF (d)EDRIN (e) RDRIN (f) Proposed

method

27

6.2.2 Comparison Table for 384 x 512 Peppers Image

Noise Density

in %

DWMF EDRIN RDRIN Proposed Method

30 18.1818 25.5927 27.0006 33.9014

40 15.9346 22.2179 23.1923 33.0055

50 14.1109 19.0566 20.0746 31.6128

60 12.6763 16.5281 17.2199 30.2138

70 11.4586 14.3798 14.7532 27.2174

Table 6.3. Comparison of PSNR values of proposed technique with

existing techniques for 384x512 peppers image

Table 6.4. Comparison of PSNR values of proposed technique with

existing techniques for 384x512 peppers image

Noise

Density in

%

DWMF EDRIN RDRIN Proposed

Method

30 988.3335 179.3965 129.7239 26.4814

40 1658.2 390.2002 311.7806 32.5482

50 2523.4 808.0132 639.1722 45.8543

60 3511.2 1446.3 1233.4 61.9017

70 4647.5 2371.9 2176.5 123.4067

29

Figure.6.3. Simulation results for 60% corrupted cameraman image

(a) original image (b) noisy image (c) DWMF (d)EDRIN (e) RDRIN (f)

Proposed method

28

Figure.6.2. Simulation results for 60% corrupted peppers image (a)

original image (b) noisy image (c) DWMF (d)EDRIN (e) RDRIN (f) Proposed

method

30

Figure.6.4. Simulation results for 60% corrupted reghuman image

(a) original image (b) noisy image (c) DWMF (d)EDRIN (e) RDRIN (f)

Proposed method

CHAPTER 7

31

CONCLUSION

From this proposed method we obtain novel edge preserving method

for salt and pepper denoising. The method is actually a combination of

switching median filter and directional median method. The advantages of this

proposed method are the directional initialization of filtering window size and

the precision of median value. Thus, no training or tuning is required. It is

ultimate filter for denoising salt and pepper noise. Simulation results show that

our proposed method performs better than the median filter and other

conventional edge preserving method, even at a high noise level. The PSNR is

high; MAE and Processing time is low. This proposed method is a fast method

in the algorithm of removing salt and pepper noise.

7.1 FUTURE ENHANCEMENTS

In the future, we will consider using wavelet thresholding for

the image denoising and also preserve the edges of the image. This

project can also extend for the removal of random valued impulse

noise.

CHAPTER 8

32

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