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INTERNATIONAL JOURNAL OF INNOVATIVE TECHNOLOGY & CREATIVE ENGINEERING
NOVEMBER 2014VOL- 4 NO - 11
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From Editor's Desk
Dear Researcher, Greetings! Research article in this issue discusses about motivational factor analysis. Let us review research around the world this month. Everyday life throws at us an endless number of pattern recognition problems smells, images, voices, faces, situations and so on. Most of these problems we solve at a sensory level or intuitively, without an explicit method or algorithm. he techniques we considered the manipulation of the dynamic range of a given digital image to improve visualization of its contents. In this consider more general image enhancement. We introduce the concept of image filtering based on localized image sub regions (pixel neighbourhoods), outline a range of noise removal filters and explain how filtering can achieve edge detection and edge sharpening effects for image enhancement. The basic goal of image enhancement is to process the image so that we can view and assess the visual information it contains with greater clarity. Image enhancement, therefore, is rather subjective because it depends strongly on the specific information the user is hoping to extract from the image. The primary condition for image enhancement is that the information that you want to extract, emphasize or restore must exist in the image. Fundamentally, ‘you cannot make something out of nothing’ and the desired information must not be totally swamped by noise within the image. Perhaps the most accurate and general statement we can make about the goal of image enhancement is simply that the processed image should be more suitable than the original one for the required task or purpose. This makes the evaluation of image enhancement, by its nature, rather subjective and, hence, it is difficult to quantify its performance apart from its specific domain of application. It has been an absolute pleasure to present you articles that you wish to read. We look forward to many more new technologies related research articles from you and your friends. We are anxiously awaiting the rich and thorough research papers that have been prepared by our authors for the next issue. Thanks, Editorial Team IJITCE
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Contents
Face Image Analysis under Various Noisy Backgrounds Using Gaussian Median Filtering FFFFFFFF S.Prasath & Dr.S.PannirselvamFFFFFFFFFFFFFFFFFFFFFFFFF.FFFFFFFFF.[243]
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Face Image Analysis under Various Noisy Backgrounds Using Gaussian Median
Filtering [GMF] S.Prasath
Ph.D (Research Scholar),Department of Computer Science, Erode Arts & Science College (Autonomous),Erode, Tamil Nadu, India.
Email: [email protected] Dr.S.Pannirselvam
Research Supervisor & Head Department of Computer Science, Erode Arts & Science College (Autonomous),Erode, Tamil Nadu, India.
Email: [email protected]
Abstract--- Today, image processing penetrates into
various fields, but till it is struggling in quality issues.
Hence, image enhancement came into existence as an
essential task for all kinds of image processings. Various
methods are been presented for gray scale image
enhancement, especially for face image. In this paper
various filters are used for face image enhancement.
However, the authenticity of noises that might insert into
an image document will affect the performance of face
recognition algorithms. Hence, different filtering
algorithms are presented for noise elimination using
various filtering algorithm. In order to improve of the
image quality Gaussian Median filtering has been applied.
The experimental result shows that this method provides
better enhancement in term of quality when compared
with the existing methods such as Mean filter, Wiener
Filter and laplacian filter. The peak Signal Noise Ratio
(PSNR) and Mean Square Error (MSE) are been used for
similarity measures.
Keywords— Mean Filter (MF), Median Filter, Wiener Filter, Gaussian Median Filter (GMF),PSNR, MSE.
I . INTRODUCTION
In the computer era there is a rapid growth in the field of
information technology and the security system was suffering
from various issues. Today, criminals have been entered into
the field of information technology called cyber crime. Lot of
security systems has emerged to solve the various security
issues such as password, username, secret codes, but failed
due to cyber attacks. In order to overcome such security issues
the biometric system has been emerged with various features
such as face recognition, fingerprints recognition, gait, palm
print, voice, signatures etc.
Every human being can identify a faces in a scene with no
effort, with an automated system such objectives are the very
challenging one due to various factors which affects the
quality of the image. Hence, face recognition system has been
used to verify the identity of an individual. It can be
accomplished by matching process using various methods and
features such as geometric, statistical, low-level features
which are derived from face images.
Since last decades, researchers are involved on face
recognition in image processing and they achieved so many
mile stone for this. Because face recognition is the critical
stage to identify the face in images due to pose, presence or
absence of structural components, facial expression,
occlusion, image orientation. Above all, Noise is prime factor
of reducing face recognition rate. Several methods have been
evolved to increase recognition rate.
Filters are widely accepted to remove impulsive and high
frequency noise for signal and image processing. The concept
of filtering has its roots in the use of the Fourier transform for
signal processing in the so-called frequency domain. Spatial
filtering term is the filtering operations that are performed
directly on the pixels of an image. The process consists simply
of moving the filter mask from point to point in an image at
each point (x, y) and the response of the filter at that point is
calculated using a predefined relationship.
II. RELATED WORK In recent years, considerable progress has been made in
the area of face recognition with the development of many
techniques. Even these techniques perform extremely well
under various constrain, the problem of face recognition in
uncontrolled by noisy environment remains unsolved. Image
noise can originate in film grain or in electronic noise in the
input device such as scanner digital camera, sensor and
circuitry or in the unavoidable shot noise of an ideal photon
detector. Noise affects the identification of images in
authentication and also in pattern recognition process. The
identification of the nature of the noise [1] is an important part
in determining the type of filtering that is needed for
rectifying the noisy image. Noise in imaging systems is
usually either additive or multiplicative [2]. The basic types of
noise can be further classified into various forms [3] such as
amplifier noise or Gaussian noise, Impulsive noise or salt and
pepper noise, quantization noise, shot noise, film grain noise
and nonisotropic noise.
A model [4] proposed with noise removal filtering
algorithms. Most of them follows certain statistical parameters
and know the noise types. Applying various a filtering
algorithms that is sensitive to additive noise to an image that
has been degraded by a multiplicative noise which does not
provide best results. Many algorithms have been developed to
remove salt & pepper noise in document images with different
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performance in removing noise and retaining fine details of
the image.
Various filtering techniques exist to perform the inverse of
the imperfections in the degraded image [5], [6]. These
filtering techniques are application oriented. Some filtering
techniques perform better than the others techniques based on
the noise category. These filters are used in a variety of
applications [7] efficiently in preprocessing module.
III. NOISE TYPES The noise is characterized by its pattern and its
probabilistic characteristics. There is a wide variety of noise
types while this paper focus on the most important types they
are Gaussian noise, salt and pepper noise, poison noise,
impulse noise, speckle noise.
A) Gaussian Noise Gaussian noise is statistical noise that has its probability
density function equal to that of the normal distribution, which
is also known as the Gaussian distribution. In applications,
Gaussian noise is most commonly used as additive white
noise to yield additive white Gaussian noise.
B) Salt and Pepper Noise Salt and pepper noise is a form of noise typically seen on
images. It represents itself as randomly occurring white and
black pixels. Salt and pepper noise creep into images in
situations where quick transients, such as faulty switching,
take place.
C) Speckle Noise Speckle is a complex phenomenon, which degrades an
image quality. Speckle noise is a multiplicative noise. The
speckle noise follows a gamma distribution [8]. Thus,
denoising or reducing the noise from a noisy image has
become the predominant step in image processing. For the
quality and edge preservation of images we have taken
different denoising techniques into consideration.
IV. EXISTING METHODOLOGY 4.1 Filters
Generally filters are used to filter unwanted things or
object in a spatial domain or surface. In digital image
processing, mostly the images are affected by various noises.
The main objectives of the filters are to improve the quality of
image by enhancing is to improve interoperability of the
information present in the images for human visual. A general
structure of a filter mask is as follows.
Fig.1.1 Filtering Mask
Image filtering can be used for many aspects which
includes, smoothing, sharpening, noise eliminating and edge
detection etc. A filter is defined by a kernel, which
represented is a small array and applied to each pixel and its
neighbours within an image.
4.2 Frequency and Spatial Filters The frequency domain technique is based on the
convolution theorem. It decomposes an image from its spatial
domain form of brightness into frequency domain components
and is represented as the following equation �(�,�) =h( �,� )∗� (�,� ) ...... (1)
Where �(�,�) is the input image, h(�,�) is a position
invariant operator and �(�,�) is the resultant image from the
convolution theorem. �(�,�) = (�,�) (�,�) ........ (2)
Where G, H, F is the fourier transform of �, h, �
respectively. The transform H (u, v) is called transfer function
of the process. Here the edge in f(x,y) can be boosted by
using H(u,v) to emphasis the high frequency component of
F(u,v). In case of spatial filter works on pixels in the
neighbourhood of the pixel. The operation on sub image
pixels is defined using mask or filter with the same dimension.
4.3 Mean Filter (MF) The Mean Filter is a linear filter which uses a mask
over each pixel in the signal. Each of the components of the
pixels which fall under the mask are averaged together to form
a single pixel. This filter is also called as average filter. The
Mean Filter is poor in edge preserving. The Mean filter is
defined by:
Where M is the total number of pixels in the
neighborhood N. Mean filtering is a simple, intuitive and easy
to implement method of smoothing images, i.e. reducing the
amount of intensity variation between one pixel and the next.
It is often used to reduce noise in images.
The idea of mean filtering is simply to replace each
pixel value in an image with the mean value of its neighbours,
including itself. This has the effect of eliminating pixel values
which are unrepresentative of their surroundings. Mean
filtering is usually thought of as a convolution filter. Like
other convolutions it is based around a kernel, which
represents the shape and size of the neighbourhood to be
sampled when calculating the mean.
4.4 Wiener Filter The wiener filtering method requires the information
about the spectra of the noise and the original signal and it
works well only if the underlying signal is smooth. Wiener
method implements spatial smoothing and its model
complexity control correspond to choosing the window size
[9].
..... (4)
Where
H(u, v) = Degradation function
H*(u, v) = Complex conjugate of degradation function
Pn (u, v) = Power Spectral Density of Noise
Ps (u, v) = Power Spectral Density of un-degraded image
-1 -1 -1
-1 N -1
-1 -1 -1
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245
Wiener filtering is able to achieve significant noise
removal when the variance of noise is low they cause blurring
and smoothening of the sharp edges of the image. Detection of
emotions in highly corrupted noisy environment this approach
involves removal of noise from the image by the Wiener Filter
for an automatic system for the recognition of facial
expressions is based on a representation of the expression
[10].
4.5 Laplacian Filter Laplacian is a 2-D isotropic measure of the second
spatial derivative of an image. The Laplacian of an image
highlights regions of rapid intensity change and is therefore
often used for edge detection. The laplacian is often applied to
an image that has first been smoothed with something
approximating a Gaussian smoothing filter in order to reduce
its sensitivity to noise. The operator normally takes a single
gray level image as input and produces another gray level
image as output.
V. PROPOSED METHODOLOGY
By considering the inefficiency of the existing image
enhancement methods there is a need to propose a new
methodology for face image enhancement which leads to
improve the quality of the image. A novel filtering technique
is proposed with the hybridization of Gaussian filter with
Median filter.
5.1 Gaussian filtering Gaussian filters are a class of linear smoothing filter
with the weights chosen according to the Gaussian functions.
Mainly these kind filters are used to smooth the image and to
eliminate the Gaussian noises.
πσσ σπσ
2 2
2 2h(m,n)=
1e
2
1 X
2 e22m n .... (5)
From the above equation 5 shows the Gaussian filter
is separable. The Gaussian smoothing filter is very good in
noise removal in normal distribution function. This filter is
rotationally symmetric the amount of smoothening is all
direction. The degree of smoothening is governed by variance
T.
5.2 Median Filtering
Then Median filter, the most prominently used
impulse noise removing filter, provides better removal of
impulse noise from corrupted images by replacing the
individual pixels of the image as the name suggests by the
median value of the gray level The median of a set of values is
such that half of its values in the set are below the median
value and half of them are above it and so is the most
acceptable value than any other image statistics value for
replacing the impulse corrupted pixel of a noisy image for if
there is an impulse in the set chosen to determine the median
it will strictly lie at the ends of the set and the chance of
identifying an impulse as a median to replace the image pixel
is very less. A commonly used non-linear operator is the
median, a special type of low-pass filter. The median filter
takes an area of an image (3x3, 5x5, 7x7, etc.), sorts out all the
pixel values in that area and replaces the center pixel with the
median value. The median filter does not require convolution.
The best known order-statistics filter is the median filter,
which replaces the value of a pixel by the median of the gray
levels in the neighborhood of that pixel,
The original value of the pixel is included in the
computation of the median. Median filters are quite popular
because, for certain types of random noise they provide
excellent noise reduction capabilities, with considerably less
blurring than linear smoothing filters of similar size.
Fig.1.2 Process flow of GMF Model
The above figure 1.2 shows the process flow of the
proposed preprocessing technique. Initially, the input image is
selected from the standard fingerprint database. Then the
Gaussian Median filter is applied on the input image for image
enhancement of Gaussian filter and Median filter to eliminate
the noise presents in the image. In order to enhance the clarity
of the image the median filter is applied with the amplification
factor A. Finally the preprocessed image has been obtained
with better quality.
VI. SIMILARITY MEASURES
6.1 Mean Squared Error (MSE) Mean square error is given by
[ ]
2
1 1
1( , ) ( , )
M N
i iMSE g i j f i j
MN = == −∑ ∑
.... (7)
Where M and N are the total number of pixels in the
horizontal and the vertical dimensions of image, g denotes the
Noise image and f denotes the filtered image.
6.2 Peak Signal to Noise Ratio (PSNR) The peak Signal to Noise ratio is calculated by:
2
10
25510 logPSNR
M SE
=
.... (8) For the image quality measures, if the value of the
PSNR is very high for an image of a particular noise type then
is best quality image.
Input Image
Preprocessed Image
Add Noise
Gaussian Filter
Median Filter
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VII. ALGORITHM
I. PAGE SIZE
VIII. EXPERIMENTATION & RESULTS The performance of the existing filters is measured
by conducting the following procedure. The performance of
the filters is measured by applying noise on the face images.
The sample face images downloaded are used to analysis this
work. For our experiments, the sample facial images from the
standard ORL face image database are used. It contains a total
of 4000 images containing 40 subjects each with 10 images
that differ in poses, expressions and lighting conditions.
Figure 1.3 shows the sample images used in our experiments
used noise. From the figure 1.4 shows noise removed using
mean filter, wiener filter and proposed Gaussian Median filter. Original Image Noisy Image
Fig.1.3 Sample and Noisy image Mean Filter Wiener Filter
Input : Input image from IDB
Output : Preprocessed Image
Step 1: Read an image from the image database (IDB)
Step 2: Add Noise to an input image.
Step3: Apply Gaussian filter on the input image.
Step 4: Then apply Median filter on the input image.
Step 5: Calculate PSNR values.
Step 6: Repeat step 2 and step 4 for all images in
database (IDB).
Step 7: Stop
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Laplacian Filter Gaussian Median Filter
Fig.1.4 Output image
Image
Id
Mean
Filter
Wiener
Filter
Laplacian
Filter
Gaussian
Median Filter
1 43.71 8.53 55.14 49.31
2 60.73 12.71 50.48 62.40
3 33.73 9.48 36.60 42.02
4 61.09 14.0 39.03 77.64
5 37.63 8.02 29.87 41.93
6 64.72 13.44 44.35 75.56
7 55.14 13.75 40.26 57.37
8 31.96 7.34 29.81 32.66
9 42.94 10.84 31.37 36.29
10 45.24 9.56 25.89 58.13
Table 1. MSE Comparison Values
From the table 1 shows the experimented values
obtained from different preprocessing methods. It shows the
selected face image from the database. The performance was
evaluated using the Mean Square Error (MSE) and Peak
Signal Noise Ratio (PSNR) in order evaluates the quality of
the image. By the analysis of the values in the table the
Gaussian Median filter is better with less MSE and high
PSNR values. In order to evaluate the performance of the
Gaussian Median filter considered the obtained results with
the existing Mean filter, Wiener filter and laplacian filter are
shown in the following table 2.
Image
Id
Mean
Filter
Wiener
Filter
High Boost
Filter
Gaussian
Median Filter
1 31.75 28.85 30.78 35.31
2 30.33 27.12 29.26 33.06
3 32.88 28.39 27.55 37.37
4 30.30 26.69 28.47 33.75
5 32.40 29.12 30.10 37.40
6 30.05 26.87 29.79 34.09
7 30.74 26.78 28.05 33.79
8 33.11 29.50 27.69 37.50
9 31.83 27.81 28.12 36.05
10 31.60 28.35 29.33 35.58
Table 2. PSNR Comparison Values
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From the below figure 4 shows the pictorial
representation of the performance evaluated. By analysing the
obtained results the proposed model produced the best results.
Hence the Gaussian Median filter is an efficient one.
IX. CONCLUSION In this paper, the noise removal of images based on
Gaussian Median filtering has been presented. The
experimental result proves the effectiveness of this approach,
providing good PSNR values when compared to existing
methods. The performances of PSNR values of proposed
Gaussian Median filtering when compared to existing
methods Mean filter, Wiener Filter and Laplacian Filter are
investigated independently. The proposed Gaussian Median
filtering produces better results with 35.39% accuracy
compared with existing methods gives 30.05 % accuracy for
Mean filter, Wiener Filter with 27.94% accuracy and laplacian
Filter with 29.26% accuracy. Moreover, the computational
cost of the algorithm is very low. Therefore, the proposed
algorithm candidates itself for implementation is in simple
low-cost cameras or in video capture devices with high values
of resolution and frame rate. The proposed scheme is capable
of achieving at least comparable and often better performance
than existing iterative filtering techniques.
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@IJITCE Publication