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Abstract: Visual perceptions can be represented in pictorial form which can be in 2D or 3D known as Image. Collective forms of
pixels constitute a form of Image. Image can be distorted by
various types of noise which blur and loss of image details may
take place. Thus number of filtering techniques are imposed
which preserve the blurness of image and even edges but overall
cannot preserve the image details. In these existing techniques
lots of changes are performed but still denoising in images
require improvement. Nowadays soft computing techniques are
used instead of filters for the removal of noise. Fuzzy system rules
are imposed for the removal of noise but this system has various
disadvantages such as training is not provided by this system.
This is overcome by the neural network. Present paper focuses on
hybrid working of two soft computing techniques fuzzy system
and neural network which overcome the disadvantages of each
other. New technique used is named as Neuro-Fuzzy technique.
This technique overcomes the disadvantages of the existing filters
namely median filter, weighted median filter, switching median
filter, recursive median filter, adaptive median filter, averaging
median filter and so on. Comparison results are shown on the
basis of various parameters namely PSNR, MSE, RMSE
Keyword: Denoising; PSNR; MSE; RMSE; Neuro-Fuzzy
I. INTRODUCTION
Image can be defined as an artifact else which can be
said as a visual representation of the visual perceptions
recorded by camera or the scanning products. These recorded
perceptions are than further digitized for the conversion of
particular recorded data to the form which can be stored in the
memory of the computer or the storage media like hard disk,
floppy disk or the compact disk. During capturing of an image
or processing of an image noise can be edited in the image
which may distort the image and results in loss of image
details. Different types of the digital images are likely to be
intruded by number of varieties of noise named as speckle
noise, impulsive noise (Salt and pepper noise), Gaussian noise,
gamma noise etc. Image edges and corners contain high
frequency content and details. Thus image edges should be
preserved. For preservation of image details various filters are
introduced. These filters remove the noise from images but
various filters fail in protecting the sharp edges and the image
details including the information of the format along with the
time of capturing the image and its size.
Various techniques are proposed for the enhancements
of images which contribute in the denoising of image.
Filtering techniques include linear filtering and Non-linear
filtering. Without explicitly identifying noise, it is removed by
nonlinear filters. In low pass filters the upper area is occupied
by the frequency spectrum. Thus due to this assumption it is
used on the group of pixels of the spatial filters. Generally
noise is eliminated by spatial filters to a reasonable extent, but
edges in images remain invisible. There are various filters
proposed which can remove these all above disadvantages are
weighted median [39], relaxed median [18] and the rank
conditioned rank selection [6]. In the removal of salt and
pepper noise the Standard Median filtering [34] process is
useful as it replaces the central pixel value by the median pixel
values of the filtering window. Thus this filter removes the
noise along with the degrading performance as it removes the
details of the image and even the blurred edges are removed.
Proposing the Weighted median filters [28] is due to the
removal of disadvantages of the standard median filters. These
filters use the approach of the selecting the filtering window
and provide the more weight to the pixels selected. There is a
vast disadvantage of weighted median filters that these filters
have difficulty to differentiate the noisy pixels and noise free
pixels. The disadvantages of above standard and the weighted
median filter are overcome by the Switching Median filter.
Middle pixel of the filtration window is classified by the
switching pulse detector. In the case of central pixel is
corrupted than for further filtration procedure the standard
median filter is used. Thus the replacement of middle pixel is
made by the value of the median filter. Else if the central pixel
is uncorrupted it remains the same. It depends on the pulse
sensor that working of this type of filtering process is
improved and more appropriate than the standard median
filter. Switching Median filter is improved for the further
improvement in the removal of noise thus for such
improvement various filters namely median filter three states
[11] and the median filter of the multi-state [9] are proposed
which also includes the features of the weighted median filter.
As these filters can handle the complex calculations thus they
are more appropriate according to the execution process
performed than the standard median filter and the weighted
median filter. Even there are number of filter for improving
the performance such as median filter of progressive switching
[43] which performs the filtering process in the iterative
method by the detection of the corrupted pixels and then
removal of that particular pixel. Due to its iterative nature it
has a great disadvantage which is high computational
complexity. To overcome above disadvantages Adaptive
median filter [10] and Ranking median filter [1] are proposed
but still enhancement is required in images for complete
removal of noise.
Image Denoising using Improved Neuro-Fuzzy
based Algorithm
Amaninder Kaur Brar1, Vikas Wasson
2 1Research Scholar,
2Assistant Professor
Amaninder Kaur Brar et al, Int.J.Computer Technology & Applications,Vol 5 (5),1772-1779
IJCTA | Sept-Oct 2014 Available online@www.ijcta.com
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Linear filters are also introduced for the removal of
noise. Linear filters were primary tools which were initially
used for many electrical signaling and processing of image
applications due to the presence of analytical theory of design
and analysis. For the corruption of image by the AWGN [17]
type noise these filters show good performance for those
particular images. In the case of mean square error for the
preservation of images from the Gaussian noise these filters
also can be used. But the great disadvantage of linear filter is
that they cannot deal the non-linearities of the image
formation model. These filters also abolish the lines, also the
important information of the image even blur the sharp edges.
Wiener filtering [3] is example of linear filtering.
In present work new improved hybrid technique is
proposed named Neuro-Fuzzy technique which denoises the
images along with the preservation of edges and its details.
This technique is beneficial as it attacks on the noisy pixels
only and keeping the rest of pixels preserved. Hence preserves
the details of image.
In this paper the various sections are distributed as in
section II the proposed technique is described in which the
pre-processing step followed by the In section III results are
discussed in the graphical and tabular representations. Last
section IV constitutes of conclusion and future scope of the
proposed technique.
II. RELATED WORK
In literature there are various techniques and filtering
methods discussed which are proposed to denoise the image.
But each filtering method has the disadvantage which is
overcome by other and at last for denoising soft computing
techniques are implanted which preserve edges and image
details and also remove the noise from images but still some
improvements are required in these techniques which are
discussed in this literature survey.
In paper 2013Ahmed F. and Das S. [2]Removal of High Density Salt and Pepper Noise in Images with an
Iterative Adaptive Fuzzy Filter using alpha-trimmed Mean Proposed a novel adaptive, iterative, fuzzy filter for denoising
various images which are including the noisy pixels. It
operates in two stages - detection of noisy pixels with an
adaptive fuzzy detector followed by denoising using a
weighted mean filter on the good pixels in the filter window. Fuzzy filters when used in adaptive setting are simple and
quite efficient. The filter uses -trimmed mean which is effective for measure of central tendency than the classical
mean than the classical mean or median measures in the
context of impulse noise removal thus, filter is shown to be
robust to very high levels of noise, retrieving meaningful
detail at noise levels as high as 97%. Alpha-trimmed mean
computes the mean of a set of elements after trimming the top
and bottom /2 elements of the set. In paper 2012Aldinucci M. and Spampinato C.[4]A
Parallel Edge Preserving Algorithm for Salt and Pepper Image
Denoising proposed a two-phase filter for removing salt and pepper noise. Noisy pixels Identification and Noisy pixel
Restoration are two phases for removal of noise. In the initial
step, detection of the corrupted pixels is done by an adaptive
median filter. In the second phase, restoration of the pixels is
performed by the regulation method. The first step identifies
noisy pixels by means of a modified Adaptive Median Filter
(AMF) classifier; whereas second step restores them using a
variational approach which is solved by using regularization.
In this algorithm initial phase exhibits a linear execution time
with respect to the total number of pixels. However, for the
tested images, the detect phase is two-three orders of
magnitude faster than a single iteration of the second step. The
second phase shows the complexity of noisy pixels as well as
number of iterations required to reach one of the convergence
criterions. Non-noisy pixels are not changed in denoise phase
only changes are provided to the noisy pixels. Mean absolute
error is improved by this algorithm. It can overall complete in
terms of quality of restoration time and execution time.
In paper 2007 Blu T. and Luisier F. [7] The SURE-LET Approach to Image Denoising proposed a latest technique for denoising the images. SURE is the Steins Unbiased Risk Estimate. Possibility of this purposed technique
is made by the presence of the best unbiased estimate of the
Mean Square Error (MSE). MSE is calculated ratio of the
initial image acquired without noise and the denoised image in
which noise is removed. In the case of orthogonal transforms
this technique is used. By the various experimental results it is
observed that there are some improvements required in this
SURE-LET algorithm.
In paper 2001 Eng H. and Ma K. [13] Noise Adaptive Soft Switching Median Filter a filter is proposed which doesnt corporate with the fuzzification concept. This proposed filter is also known as Noise Adaptive Soft
Switching Median (NASM) filters. This technique is purposed
to achieve the filtration to the best by effectively removing the
noise along with preservation of the robustness and variations
in the noise density. There are two stages in this proposed
technique. Each pixels characteristic is identified. In the initial level the numbers of real pixels are detected as
uncorrupted pixels. In the next levels the rest pixels which are
not identified are discriminated. The discrimination is done as
such: isolated salt and pepper noise, non-isolated salt and
pepper noise or edge pixel. In the next stage the exploitation
of the fuzzy logic is done in which filtering scheme includes
no filtering to identified uncorrupted pixels known as fuzzy
weighted median (FWM). For the classification of the each
pixel being noise free a scheme is developed namely soft-
switching noise-detection.
In paper 2010 Fazli S. et.al [14] Complex PDE Image Denoising Based on Particle Swarm Optimization Artificial Intelligence techniques are used along with Partial Differential
Equations (PDE) for denoising of image. This technique is
different from the previous techniques using PDE as in this
method for the various complicated PDE parameters Particle
Swarm Optimization (PSO) is used. These PDE parameters
are tuned by reducing the Structural Similarity (SSIM)
measure. Proposed method obtains the simulation results of
standard images. Edge shock filter is present in PDE for
sharpening the edges.
In paper 2007 Feng D. et.al. [15] High Probability Impulse Noise-Removing Algorithm Based on Mathematical
Morphology a novel filter is proposed for the large contingency removal of the impulsive noise. Initially, the
Amaninder Kaur Brar et al, Int.J.Computer Technology & Applications,Vol 5 (5),1772-1779
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impulsive pixels (noisy pixels) are identified by the impulse
noise detector by using the mathematical residues. Block
smart erase method is used to remove the back and white
blocks which may reduce the image quality. Open close
sequence filter is applied in this approach. This filter is used in
such a way that opening of the filter results in the removal of
the salt impulsive noise and removal of the pepper impulsive
noise is caused by the closure of the filter. Thus this filter is
successful in the removing the impulsive noise in the images
by removing black and white spots separately, which makes
this process more impressive in denoising of the image.
In paper 2013 Gargouri A. and Masmoudi D.S [16]
Neural Network Based image denoising with Pulse Mode Operations and Hybrid on-chip learning algorithm proposed a pulse mode neural network (PMNN) based image denoising
operation. In the purposed approach a hybrid learning
algorithm, in which, they applied the K-means algorithm to
adjust the centers positions of the basic activation functions, as
well as the back-propagation algorithm to update the
connection weights. However, early pulse mode
implementation suffers from some constraints due to the
complexity of the on-chip learning ability, since the back-
propagation algorithm is probably the most used, which costs
much of hardware resources. In this paper hardware
implementation is presented of Radial Basis Function (RBF)
Neural Network based removal of image. In this image
denoising function was applied. There is an ability of
proposed algorithm to gain the complete information from the
corrupted or infected image, with respect to the PSNR metric.
In paper 2009 Kumar V. et al. [24] Robust Statistics Based Algorithm to Remove Salt and Pepper Noise in Images a robust estimation filter is purposed for the removal of the
impulse noise up to the noise density of 70% efficiently. This
filtration process is performing better comparatively to the
previous filters discussed earlier such as standard median
filter, weighted median filter, recursive weighted median filter
and the progressive switching median filter along with the
decision based algorithm. The function of algorithm is the
detection of the noisy pixel first as certain pixels are noisy by
salt and pepper noise while remaining pixels remains
uncorrupted. This will satisfactorily remove the impulsive
noise density till the medium level along with the preservation
of edges of an image.
In paper 2006Luo W. [26] An Efficient Detail-Preserving Approach for Removing Impulse Noise in Images Presents an algorithm which preserves image details and
removes the impulsive noise from the noisy images. This
algorithm is based on the alpha-trimmed mean. This algorithm
consists of the specific case of the order-statistics filter.
Instead of pixel value estimation this process is used in
detection of the impulsive noise. When identification of the
corruptive image is made, replacement of its value is done by
the continuous succession of the initial value and the median
calculated for the local window. In proposed algorithm the
filtering process is applied only to corruptive pixels either to
all pixels in an image. Purposed algorithm consists of three
steps: Impulse noise detection, refinement and impulse noise
cancellation. Thus by following above steps this algorithm
replacement is made of the values which are identified as
noisy pixels with the predicted values. As the yielded
subjective quality is best with the consideration to the
impulsive noise suppression and the protection of the image
details thus it is said that performance is better than earlier
existing median filters.
In paper 2012 Thirilogasundari.V, Suresh babu.V,
Agatha Janet.S [34] Fuzzy Based Salt and pepper noise removal using adaptive switching median filter proposed a new filter which concentrate on removal of impulse noise
caused due to fault in memory units, imperfections in
transmission medium, errors in transmission system. This
filter overcome the drawbacks of previous systems such as this
system removes only noisy or corrupted pixels and preserves
the pixels which are noise less. Proposed system is based on
switching median filter. It consists of two stages namely:
detection stage and filtering stage. Neighborhood mapping
based algorithm detects the corrupted pixels in detection stage
and in next stage of filtering fuzzy membership function is
used. This is compared by the existing techniques by various
parameters. This proposed algorithm has various advantages
on the removal of the impulsive noise along with protection of
the other image details, edges and textures and neither require
further tuning but same filtering process cannot be used in
case of other type of noise removal such as Gaussian noise etc.
In paper 2010 Wang X. et.al [36] Image Denoising Based on Translation Invariant Directional Lifting for the reduction of artifacts in various denoising results translation
invariant directional lifting is proposed by employing the
cycle spinning based technique. For the achievement of the
better results with the lower intricacy in this technique 2-D
Gabor filter is adopted which also provide better denoising
results. The results are calculated in the form of PSNR value
for objective and SSIM evaluation which is subjective
performance.
In paper 2001Wu H.R. and Chen T. [38] Adaptive Impulse Detection Using Centre-Weighted Median Filters adaptive impulse detector was proposed in which centre
weighted median filter is used for the efficient ejection of
impulsive noise. In this paper a novel adaptive operator is
devised in which estimation is based on the difference
between the initial pixel and the output results of centre
weighted median filters. Performance of this method is better
only in the case of image corruption till the 50% with the
impulsive noise.
In paper 2012 Yang X. et.al [40] Image Denoising Based on Support Vector Machine presents the algorithm which performs denoising of an image and prevents the fine
information of the image. The support vector machine based
method is used for the removal of noise and is perfect method
for denoising as it retains the image details along with the
removal of the noise. Support vector machine is a machine
learning which is based on the statistical learning theory and
thus this method solves various classification problems. In this
method wavelet transform is used which consists of various
signaling and performing on the different forms of wavelet
transforms for the construction of the corresponding rules.
In paper 2009 Zaho T. et.al [41] Approach of Image Denoising Based on Discrete Multi-Wavelet Transform by using multi-wavelet transform new approach was proposed to
perform remote sensing image denoising. Extraction of the
signal from the corrupted data a rise is given to wavelet
Amaninder Kaur Brar et al, Int.J.Computer Technology & Applications,Vol 5 (5),1772-1779
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thresholding method by wavelet theories. Multi-wavelets are
made more appropriable for various image processing
(denoising) by orthogonality, symmetry and short support
properties. This technique is better than the previous
techniques as less distortion of spatial characteristics occur in
image denoising. Wavelet based method perform both in
objectively and subjectively form as the result of experiments
performed.
In paper 2002 Zhang S. and Karim A. [44] A New Impulse Detector for switching Median Filters proposed a latest technique for detecting impulse in which switching
median filters are present. These median filters are based on
the least absolute value for the four convolutions. In this
presented filtration the focus is made over the preservation of
the lines and provides improved detection of the black and
white pixels means distorted pixels. As the previous median
filters were unable to differentiate between the thin lines and
the impulses but the presented filtration method distinguish
between these thin lines and impulses and hence is successful
in removal of the thin lines which results in better
performance than the existing filtering methods. Noise is
measured by the MSE curves which show increase of noise
(after 30% approx.) by significant increase in graph. Still lots
of improvement is required in impulse detector as in this
process normalization is required thus the performance of
filter can be improved by making various significant changes.
III. PROPOSED ALGORITHM
A. Detection of noisy pixel in an input acquired image: An
image is acquired by input source such as camera. A range is
estimated in which the uncorrupted pixels may lie. The pixels
which not lie in an estimated range are detected as noisy or
corrupted pixels.
Algorithm: A filtering window is slide over the highly
contaminated image.
ALGORITHM: SLIDING WINDOW
BEGIN
1: ACQUIRE (IMAGE)
2: ADD NOISE (GAUSSIAN NOISE, SALT AND PEPPER NOISE, SPECKLE NOISE)
3: SELECT 2*2 WINDOW (N)
4: SLIDE N
5: IF
NEIGHBORING PIXELS SELECTED INCREASED
SELECT 3*3 WINDOW
6: ELSE
SELECT 5*5 WINDOW
7: ELSE
SELECT 7*7 WINDOW
PIXEL DETECTED (P)
END
During sliding the window the noisy pixels are detected by
calculating the maximum, minimum and the median values.
ALGORITHM: DETECTED CORRUPTED PIXELS
BEGIN
1: CALCULATE (MAX, MIN, MED)
2: IF
MAX
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Filter and Adaptive Median Filter. This comparison process is
performed by using various performance parameters such as
PSNR, MSE and RMSE. Algorithm for PSNR (Peak Signal to
Noise Ratio), MSE (Mean Square Error) and RMSE (Root
Mean Square Error) is shown below.
Algorithm: Calculation of PSNR, MSE and RMSE
ALGORITHM: PSNR (PEAK SIGNAL TO NOISE RATIO),
RMSE (ROOT MEAN SQUARE ERROR)
WHERE MSE MEAN SQUARE RATIO
BEGIN
1: INPUT ARRAY (X, Y)
2: SIZE OF ARRAY= N*M
3: =1
, (, ) 2 1=0
1=0
4: USING STEP 3 CALCULATE PSNR
5: = 1010 2
6: USING STEP 3 CALCULATE RMSE
7: =
END
Figure 1: Proposed Methodology
IV. RESULTS AND DISCUSSIONS
This section includes the results which are obtained after
implementing Neuro-Fuzzy technique which denoise the
images from the various types of noise. In the initial part of
this section screenshots representing different outputs by the
different filtering techniques and in the next section various
parameters used for performance measurement is presented
along with the comparison results for various filtering
techniques with the different parameters in tables. Finally for
better understanding of the results these tabular form results
are presented in form of Graphs.
Initially image is acquired or selected from the number
of dataset. After the selection of image, addition of noise is
made to an image. Gaussian, Salt & pepper or Speckle noise
either can be edited to the image. Finally the filter is selected
from the image with which the image is to be filtered. These
all filters are introduced by using Matlab implementation
A. Denoised Image Outputs
(a) Original Image
(b) Filtered Image by Median Filter
(c) Filtered Image by Averaging Filter
(d) Filtered Image by Neuro-Fuzzy
Technique
B. Comparison Tables
In this section comparison of various techniques is shown by
the various parameters namely MSE, RMSE and PSNR. The
tabular results are represented such as:
Acquire an Image
Noise is added in Image
Filtering Window is slide over the
corrupted Image
Corrupted pixels are detected
Uncorrupted pixels
are kept as such
Noise is removed
from corrupted
pixels using Neuro-
Fuzzy Technique
Denoised Image
Amaninder Kaur Brar et al, Int.J.Computer Technology & Applications,Vol 5 (5),1772-1779
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Table 1: Performance of MSE for different filtering technique on pepper
image
NOISE
DENSITY %
50%(NOISE
DENSITY)
80%(NOISE
DENSITY)
90%(NOISE
DENSITY)
MEDIAN
FILTER
492.4 973 1253.02
AVERAGING
FILTER
369.2 873.34 1053.67
ADAPTIVE
FILTER
257.1 480.06 986.0
PROPOSED
NEURO-FUZZY
149.79 297.99 640.42
AVERAGE 317.12 656.09 983.28
Table 2: Performance of PSNR for different filtering techniques on pepper
image
NOISE
DENSITY %
50%(NOISE
DENSITY)
80%(NOISE
DENSITY)
90%(NOISE
DENSITY)
MEDIAN
FILTER
23.20 19.48 18.19
AVERAGING
FILTER
25.68 20.43 17.65
ADAPTIVE
FILTER
28.02 26.31 25.38
PROPOSED
NEURO-FUZZY
33.32 28.27 26.69
AVERAGE 27.51 23.62 21.98
Table 3: Performance of RMSE for different filtering techniques on pepper
image
NOISE
DENSITY %
50%(NOISE
DENSITY)
80%(NOISE
DENSITY)
90%(NOISE
DENSITY)
MEDIAN
FILTER
22.20 31.19 35.39
AVERAGING
FILTER
19.21 29.55 32.46
ADAPTIVE
FILTER
16.03 21.91 31.40
PROPOSED
NEURO-FUZZY
12.24 17.26 25.30
AVERAGE 17.42 24.98 31.13
C. Comparison Graphs
In this section comparison of various tabular results is
represented in the graphical form which shows the results
clearly. These graphical representations are such as:
Graph 1: Graphical representation of MSE for different filtering techniques on pepper image.
Graph 2: Graphical representation of PSNR for different filtering technique
pepper image
Graph 3: Graphical representation of RMSE for different filtering techniques
on pepper image
V. CONCLUSION AND FUTURE SCOPE
A. Conclusion
Amaninder Kaur Brar et al, Int.J.Computer Technology & Applications,Vol 5 (5),1772-1779
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In this research work a new hybrid technique is described
which is known as Neuro-Fuzzy technique. In this technique
combined features of neural network and fuzzy technique are
applied for the removal of noise to preserve image details and
edges. Thus this proposed filter is seen a quite effective in
eliminating different types of noise such as salt and pepper
noise, Gaussian noise and speckle noise along with preserving
the edges. The efficacy of this technique is illustrated by
applying on number of test images as shown in above results
and discussions. In terms of qualitative and quantitative
measures proposed technique outperforms the existing based
filters with which it is compared such as Median filter,
averaging filter, adaptive filter etc. the images filtered are
quite pleasant for visual perception. It is also quite suitable for
the real time implementations due to its adaptive nature.
B. Future Scope
The proposed technique removes the noise along with
preservation of edges quite efficiently and effectively. But as
neural network requires training of the system which takes
some time to complete the training of the system. Along with
the increment of layers of neural network the training time
increases thus some improvement is required to decrease the
time of the training or learning process.
Present work is done on 2-D images but in future this can
be processed on the 3-D images
In future the time for training of neural network can be
reduced to more extent.
As the present technique is implemented on various sample
images but in future this removal technique can be
implemented on various videos.
ACKNOWLEDGMENT
I would like to express the deepest appreciation to my
supervisor Er.Vikas Wasson, for the useful comments,
remarks and engagement through the learning process of my
research. Without his guidance and persistent help this thesis
would not have been possible. His patience, motivation,
enthusiasm, support and immense knowledge helped me to go
through my research and finish this research paper. His
guidance helped me in all the time of research and writing of
this paper. I hope that one day I would become as good an
advisor to my students as he has been to me. I would also like
to express gratitude to Chandigarh University for providing
me great opportunity to share my innovative ideas on topic
Image Denoising using Improved Neuro-Fuzzy based Algorithm.
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