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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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