i jc ta 2014050532

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 [email protected]

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ISSN:2229-6093

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



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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



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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

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



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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



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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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