A Fast and Robust Hybridized Filter For Image De-Noisingijsetr.org/wp-content/uploads/2014/12/IJSETR-VOL-3... · Filter, Order Static Filter, SVM, PSNR, MAE. I. INTRODUCTION Images

International Journal of Science, Engineering and Technology Research (IJSETR), Volume 3, Issue 12, December 2014

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ISSN: 2278 – 7798 All Rights Reserved © 2014 IJSETR

A Fast and Robust Hybridized Filter For Image De-Noising

1Ramandeep Kaur

1Student of M.Tech IT, Guru Kahi University,

Talwandi Sabo, India

2Er.Rachna Rajput

2Assistant Professor, CSE, Guru Kashi University,

Talwandi Sabo, India

Abstract - Salt & pepper noise degrades the quality

of the image by hiding the details of objects in the

image and also causes damages to the colour

quality of an image. Noise removal is an important

task in image processing. The noise has to be

removed to obtain the good quality image after

removing the salt and pepper noise. But the

existing salt & pepper noise removal filter like

median, wiener and order static filters are not

capable of reproducing object details in image with

higher accuracy. In this Dissertation work, I have

developed a new hybridized filter for the removal

of salt & pepper noise. This proposed filter will

remove the noise with minimum image quality

degradation. I propose the development of an

advanced salt & pepper noise removal filter using

effective statistic and image processing methods to

remove the noise along with support vector

machine (SVMs) that is effectively do the job by

reproducing the deep image details after removing

the noise, which enhance the quality of image than

the existing filters. The results presented show that

this filter slightly outperforms previous salt and

pepper filters, both in quality and in edge

preservation. To compare results of all existing

filters with new hybridized filter, I use comparison

parameters like PSNR, and MAE.

KEYWORDS– Salt and Pepper Noise, Median

Filter, Order Static Filter, SVM, PSNR, MAE.

I. INTRODUCTION

Images taken with both digital cameras and

conventional film cameras will pick up noise from

a variety of sources. Many further uses of these

images require that the noise will be (partially)

removed – for aesthetic purposes as in artistic work

or marketing, or for practical purposes such as

computer vision. There are various types of noise

which can affect an image such as Salt and Pepper

noise, Gaussian noise, Shot noise etc. In salt and

pepper noise (sparse light and dark disturbances),

pixels in the image are very different in color or

intensity from their surrounding pixels; the defining

characteristic is that the value of a noisy pixel bears

no relation to the color of surrounding pixels [1].

Generally this type of noise will only affect a small

number of image pixels.

When viewed, the image contains dark and white

dots, hence the term salt and pepper noise. Typical

sources include flecks of dust inside the camera and

overheated or faulty CCD(charge-coupled device)

[2].

Image processing is the study of any algorithm that

takes an image as input and returns an image as

output. Image processing is any form of signal

processing for which the input is an image, such as

a photograph or video frame the output of image

processing may be either an image or a set of

characteristics or parameters related to the image.

The digital image is processed by a computer to

achieve the desired result. Image enhancement

improves the quality (clarity) of images for human

viewing. Removing blurring and noise, increasing

contrast, and revealing details are examples of

enhancement operations. For example, an image

might be taken of an endothelial cell, which might

be of low contrast and somewhat blurred. Reducing

the noise and blurring and increasing the contrast

range could enhance the image. The original image

might have areas of very high and very low

intensity, which mask details. An adaptive

enhancement algorithm reveals these details.

Adaptive algorithms adjust their operation based on

the image information (pixels) being processed. In

this case the mean intensity, contrast, and sharpness

(amount of blur removal) could be adjusted based

on the pixel-intensity statistics in various areas of

the image.

An image may be described as a two-dimensional

function

I=f(x, y)

Where x and y are spatial coordinates. Amplitude

of f at any pair of coordinates (x, y) is called


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intensity I or gray value of the image. When spatial

coordinates and amplitude values are all finite,

discrete quantities, the image is called digital

image. Digital image processing may be classified

into various sub branches based on methods whose:

• Inputs and outputs are images.

• Inputs may be images where as outputs are

attributes extracted from those images.

Digital images are form of visual information

captured or transmitted using camera or other

imaging system. The received image might be

corrupted due to the presence of noise. Image noise

reduction without structure degradation is perhaps

the most important low-level image processing

task. Faulty sensors, optic imperfectness,

electronics interference, and data transmission

errors may introduce noise to digital images. In

considering the signal-to-noise ratio over practical

communication media, such as microwave or

satellite links, there can be degradation in quality

due to low received signal power. Based on

trichromatic color theory, color pixels are encoded

as three scalar values, namely, red, green and blue

(RGB color space). Since each individual channel

of a color image can be considered as a

monochrome image, traditional nonlinear image

filtering techniques often involved the application

of scalar filters on each channel separately.

However, this disrupts the correlation that exists

between the color components of natural images.

As such the color noise model should be considered

as a 3-channel perturbation vector in color space.

Image noise is the random variation of brightness

or colours information in images produced by the

sensor and circuitry of a scanner or digital camera.

Image noise can also originate in film grain and in

the unavoidable shot noise of an ideal photon

detector. Although these unwanted Fluctuations

became known as "noise" by analogy with

unwanted sound they are inaudible and such as

dithering.

The types of Noise are following:-

• Amplifier noise (Gaussian noise)

• Salt-and-pepper noise

• Speckle noise etc.

Salt and pepper noise also called as an impulse

noise. It is also referred to as intensity spikes.

Mainly while transmitting data we will get this salt

and pepper noise. It has only two possible values, 0

and 1. The probability of each value is typically

less than 0.1. The corrupted pixel values are set

alternatively to the maximum or to the minimum

value, giving the image a “salt and pepper” like

appearance as salt looks like [13]white(one) and

pepper looks as black(zero) for binary ones. Pixels

which are not affected by noise remain unchanged.

For an 8-bit image, the typical value for pepper

noise is 0(minimum) and for salt noise

255(maximum). This noise is generally caused in

digitization process during timing errors,

malfunctioning of pixel elements in the camera

sensors, faulty memory locations.

SUPPORT VECTOR MACHINE

Support Vector Machine (SVM) was first heard in

1992, introduced by Boser, Guyon, and Vapnik in

COLT-92. Support vector machines (SVMs) are a

set of related supervised learning methods used for

classification and regression [5]. They belong to a

family of generalized linear classifiers. In another

terms, Support Vector Machine (SVM) is a

classification and regression prediction tool that

uses machine learning theory to maximize

predictive accuracy while automatically avoiding

over-fit to the data. Support Vector machines can

be defined as systems which use hypothesis space

of a linear functions in a high dimensional feature

space, trained with a learning algorithm from

optimization theory that implements a learning bias

derived from statistical learning theory. Support

vector machine was initially popular with the NIPS

community and now is an active part of the

machine learning research around the world. SVM

becomes famous when, using pixel maps as input;

it gives accuracy comparable to sophisticated

neural networks with elaborated features in a

handwriting recognition task [9]. It is also being

used for many applications, such as hand writing

analysis, face analysis and so forth, especially for

pattern classification and regression based

applications. The foundations of Support Vector

Machines (SVM) have been developed by Vapnik

[27] and gained popularity due to many promising

features such as better empirical performance. The

formulation uses the Structural Risk Minimization

(SRM) principle, which has been shown to be

superior, to traditional Empirical Risk

Minimization (ERM) principle, used by

conventional neural networks. SRM minimizes an

upper bound on the expected risk, where as ERM

minimizes the error on the training data. It is this

difference which equips SVM with a greater ability


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to generalize, which is the goal in statistical

learning. SVMs were developed to solve the

classification problem, but recently they have been

extended to solve regression problems [5].

II. Median filter

A digital filter is a system that performs

mathematical operations on a sampled. There are

two types of filters that are used to remove

different type of noises from digital images.

Linear filters are used to remove certain type of

noise. Gaussian or Averaging filters are suitable for

this purpose. These filters also tend to blur the

sharp edges, destroy the lines and other fine details

of image, and perform badly in the presence of

signal dependent noise. Non-linear filters have

quite different behaviour compare to linear filters.

For non-linear filters, the filter output or response

of the filter does not obey the principals of scaling

and shift invariance. In this project I use Average

filter, and Median filter. Average filter is linear

filters and a median filter is a non-linear filter.

Image de-noising is a vital image processing task

i.e. as a process itself as well as a component in

other processes. There are many ways to de-noise

an image or a set of data and A method exists. The

important property of a good image de-noising

model is that it should completely remove noise as

far as possible as well as preserve edges.

Traditionally, there are two types of models i.e.

linear model and non-liner model. The benefits of

linear noise removing models is the speed and the

limitations of the linear Models are the models are

not able to preserve edges of the images in a

efficient manner i.e the edges, which are

recognized as discontinuities in the image, are

smeared out. On the other Hand, Non-linear models

can handle edges in a much better way than linear

models.

Figure: 1 Types of Filter

A median filter comes under the class of nonlinear

filter. The best known order statistics filter is the

median filter that replaces the value of a pixel by

the median of their neighbourhood pixels.Median

Filters are very effective in removing impulse noise

at low density levels.

The median filter follows the moving window

principle for filtering. A 3 × 3, 5 × 5 or 7 × 7 kernel

of pixels is scanned over pixel matrix of the

complete image. The median of the pixel values

within the window is computed, and therefore the

center pixel of the window is replaced with the

computed median. Median filtering is completed

by, initial sorting all the pixel values from the

surrounding neighbourhood into numerical order so

substitution the pixel being considered with the

centre pixel value. Note that the median value must

be written to a separate array or buffer in order that

the results are not corrupted because the method is

performed.

The below process illustrates the methodology of

median filtering. 5x5 mask and the pixel values of

image in the neighbourhood of considered noisy

pixel are

1

2

5

1

4

7

1

7

5

1

1

1

1

5

0

1

2

0

1

1

5

1

5

0

1

0

8

1

1

8

1

2

2

1

3

2

1

4

0

1

0

7

1

1

2

1

1

2

1

5

2

1

2

8

1

3

4

1

1

2

1

3

4

1

5

5

1

5

5

1

9

8

1

4

5

Table: 1 Median Values in the Neighbourhood Of

140

Algorithm for Median Filter

1. Step 1. Select a two dimensional window W of

size 3*3. Assume hat the pixel being processed

is Cx,y.

2. Step 2. Compute Wmed the median of the

pixel values in window W.

3. Step 3. Replace Cx,y by Wmed.

4. Step 4. Repeat steps 1 to 3 until all the pixels

in the entire image are processed.


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Advantage:

a. It is easy to implement.

b. Used for de-noising different types of noises. [5]

Disadvantage:

a.Median Filter tends to remove image details

while reducing noise such as thin lines and corners.

b. Median filtering performance is not satisfactory

in case of signal dependant noise. To remove these

difficulties different variations of median filters

have been developed for the better results.

III. PARAMETERS ANALYZED

In order to determine the performance of various

noise removal algorithms the following parameters

are analyzed:

A.Peak Signal-to-Noise Ratio

B.Mean Absolute Error

A. Peak Signal-To-Noise Ratio

Peak signal-to-noise ratio, often abbreviated PSNR,

is an engineering term for the ratio between the

maximum possible power of a signal and the power

of corrupting noise that affects the fidelity of its

representation. Because many signals have a very

wide dynamic range, PSNR is usually expressed

in terms of the logarithmic decibel scale. The

PSNR is most commonly used as a measure of

quality of reconstruction of lossy compression

codecs (e.g., for image compression). The signal in

this case is the original data, and the noise is the

error introduced by compression. When comparing

compression codecs it is used as an approximation

to human perception of reconstruction quality,

therefore in some cases one reconstruction may

appear to be closer to the original than another,

even though it has a lower PSNR (a higher PSNR

would normally indicate that the reconstruction is

of higher quality) [6].

where MAX is the maximum possible pixel value

of the image.

b. Mean Absolute Error

It is a quantity used to measure how close forecasts

or predictions are to the eventual outcomes. The

mean absolute error is given by

As the name suggests, the mean absolute error is an

average of the absolute error

,

Where are the prediction and the true value.

The mean absolute error is a common measure

of forecast error in time series analysis, where the

terms "mean absolute deviation" is sometimes used

in confusion with the more standard definition

of mean absolute deviation

IV. PROPOSED MODEL

In this research, we will work on the development

of a new method for the removal of salt & pepper

noise by creating a new hybridized filter using

existing and/or new noise removal filters. The

proposed filter will remove the noise with no or

minimum image quality degradation. Salt & pepper

noise degrades the quality of the image by hiding

the details of objects in the image and also causes

damages to the colour quality of an image. The

noise has to be removed to obtain the good quality

image after removing the salt and pepper noise. But

the existing salt & pepper noise removal filter like

median filter are not capable of reproducing object

details in image with higher accuracy. We propose

the development of an advanced salt & pepper

noise removal filter using effective statistic and

image processing methods to remove the noise

along with support vector machine (SVMs) that

will effectively do the job by reproducing the deep

image details after removing the noise, which will

enhance the quality of image than the existing

filters.

Algorithm for Hybridized Filter

1. Img Load Image ()

2. FiltSvm Load SVM Filter

3. ImageRestored FiltSvm(Img)

4. Allocate outputPixels ()

a. If not lastSegment(feof(blockSize))

b. clrArr ColorArray()

c. abrIdx abnormIndex(clrArr)

d. nIdx neuralize(clrArr,abrIdx)

5. sortClrArr sort(nIdx)

6. outputPixel level(sortClrArr)

This proposed method will help pathologists

identify the characteristic of pyloric stenos is. For

this reason, the paper is separately as follows. First,

as mention in introduction, the brief review about

medical image is guided and summary about filters

technique. Second is methodology of doing the

tasks. Third, the three example tests based on these

http://en.wikipedia.org/wiki/Forecast_error

http://en.wikipedia.org/wiki/Time_series_analysis

http://en.wikipedia.org/wiki/Mean_absolute_deviation


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techniques are tried. Meanwhile, we also made the

comparisons with median filter, wiener filter, and

order-static filter. Finally, some conclusions are

made and discussed.

Hybridized De-Noising filter Flow Chart

1: Flow chart for Hybridized De-Noising filter

In results we are taking an original coloured image

and then convert it into grey scale image that is

black and white image. Then we are adding salt and

pepper noise into the original image.

Figure 1: Showing Original Image and its planes

In figure 1 we are taking an original Colour image.

Image name is tomato. Then we show its three

RGB planes these planes are red, green and

blue.RGB means Red, Green and Blue planes.

Figure 2: Images with salt and pepper noise on

different planes

Now we are adding salt and pepper noise into an

original coloured image. Then we display results on

the image RGB (Red, Green, Blue) planes. salt and

pepper noise is present in the image in the form of

black and white dots. Which corrupt the image and

hide the details of an image.

Image Acquisition

Load SVM Image Filter

Apply SVM Image Filter

Allocate output pixels and

return image

Extract color array

Extract Abnormal Index

Neuralize the color array on

the basis of abnormal index

Sort and level the image

matrix and return de-noised

image


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Figure 3: Results of Median filtering

In this figure we are showing results of median

which is used for noise reduction on different

planes of an image.

Median filter is a spatial filtering operation, so it

uses a 2-D mask that is applied to each pixel in the

input image. It is used to remove defects and noise

from pictures.

Median filter is much less sensitive than the mean

to extreme values (called outliers).

Figure 4: Results of Order-static filtering

In this figure we are showing results of order static

filter which is used for noise reduction on different

planes of an image. The classical order-static

masking is the method that using nonlinear Filter.

The order-static masking method operates by

adding a fraction of the high pass filtrated version

of the input image to the original one. This operator

is sensitive to noise due to the presence of the

linear high pass filter which cannot discriminate

signal from noise. Moreover it perceptually

enhances image more in dark areas than in lighter

ones.

Figure 5: Result of Hybridized Filter

This is new filter named as hybridized filter used to

de-noising the image and its different planes.

Figure 6: Results SVM

In figure 5 we are using SVM (Support Vector

Machine) for noise estimation into the image.


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Figure 7: Comparisons of all filters

In figure 7 Results of the various image de-noising

filters have been shown. The images are added up

with noise during the acquisition or transmission

processes. In order to remove the noise from the

images, the image matrix has to be normalized. The

normalization of the images is also called as image

de-noising. The filters used in this research are

order static, median and the Hybridized filter. The

results have proved the effectiveness of the

Hybridized filter when compared to the other

image de-noising filters.

VII. COMPARISONS OF ALL FILTERS

We are comparing the results of filters with MAE

AND PSNR Comparison Parameters.

For image 1:

Table 1: Comparison of Filters for image1

Filter Name MAE PSNR

Median Filter: 1.269264 78.934946

Order Static

Filter

22.618818 75.480454

hybridized

Filter

-0.222963 82.561588

For image 2

Table 2: Comparison of Filters for image 2

Filter Name MAE PSNR

Median Filter: 4.738779 94.629591

Order Static

Filter

37.964697 91.175099

hybridized

Filter

4.119984 98.256233

These tables are shows the comparison of all filters

with comparison parameters PSNR and MAE

values for image1. By these values we get better

results with Hybridized filter then Median and

Order static filter. Hybridized filter is smoother and

shows the details of image than the other ones.

VIII. CONCLUSION AND FUTURE SCOPE

1 CONCLUSION

Salt & pepper noise degrades the quality of the

image by hiding the details of objects in the image

and also causes damages to the colour quality of an

image. The noise has to be removed to obtain the

good quality image after removing the salt and

pepper noise. But the existing salt & pepper noise

removal filter like median filter, order static are not

capable of reproducing object details in image with

higher accuracy. In this dissertation, I create a new

filter that is hybridized filter using existing for the

removal of salt & pepper noise. The proposed filter

will remove the noise with no or minimum image

quality degradation. We propose the development

of an advanced salt & pepper noise removal filter

using effective statistic and image processing


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methods to remove the noise with hybridized filter

which will enhance the quality of image than the

existing filters. Quality of image is also be

improved and values of PSNR and MAE are also

give better results as compare to the values of

existing filters.

2 FUTURE SCOPE

In the future, various techniques can be considered

to incorporate in this scheme to further improve the

performance and preserve more edges in both

highly and lowly corrupted images. We also can

develop a filter that can completely remove high

density noise from an image and work on the

details of an image. In future any one can improve

the performance of de-noising filter and also show

improvement in Comparison parameters values like

PSNR, MSE, and MAE. To improve de-noising

along the edges as the method we used did not

perform so well along the edges. In future salt and

pepper noise is also removing from audio and video

signals.

REFERENCES

[1] Amandeep Kaur, Karamjeet Singh (2010),

“Speckle Noise Reduction By Using Wavelets”,

NCCI 2010 -National Conference On

Computational Instrumentation Chandigarh, India.

[2] Abhishek Kesharwani Sumit Agrawal Mukesh

Kumar Dhariwal (2013), “An Improved Decision

based Asymmetric Trimmed Median Filter for

Removal of High Density Salt and Pepper Noise”,

International Journal of Computer Applications

(0975 – 8887) Volume 84 – No 8.

[3] Chandra Sekhar Panda, and Prof. (Dr.)

SrikantaPatnaik (2010), “Filtering Corrupted

Image and Edge Detection in Restored Grayscale

Image Using Derivative Filters”, International

Journal of Information Processing, Volume (3):

Issue (3).

[4] Chanchal Srivastava, Saurabh Kumar And O.P.

Singh (2013), “performance comparison of various

filters and wavelet transform for image de-noising”

IOSR Journal of computer Engineering (IOSR-

JCE), p-ISSN: 2278-8727Vol. 10,Issue. 1.

[5] Er. Nancy, Er. Sumandeep Kaur (2013),

“Comparative Analysis and Implementation of

Image Enhancement Techniques Using MATLAB”

International Journal of Computer Science and

Mobile Computing, Vol. 2, Issue.4.

[6] Irphan Ali Shaik, 2Mirza shafi shahsavar,

3K.J.Silva Lorraine, 4Ajesh kumar vishwanadham

(2012), “Impulse Noise Detection and Filtering

Based on Adaptive Weighted Median Filter” ISSN:

2278-4721, Vol. 1, Issue 8.

[7] J.Harikiran, B.Saichandana, and B.Divakar

(2010), “Impulse Noise Removal in Digital

Images”, International Journal of Communication

Systems, Volume 10– No.8, 39-42.

[8] Mrs. C. Mythili and Dr. V. Kavitha (2011),

“Efficient Technique for Color Image Noise

Reduction”, TRBJ ACM - ISWSA; ISSN: 2078-

7952 (print); 2078-7960 (online).

[9] Mr. Salem Saleh Al-amri, Dr. N.V.

Kalyankarand Dr.Khamitkar S.D (2010), “A

Comparative Study of Removal Noise from

Remote Sensing Image” IJCSI International

Journal of Computer Science Issues, Vol. 7, Issue.

1, No. 1.

[10] Om Prakash Verma and Shweta Singh

(2013),“A Fuzzy Impulse Noise Filter Based on

Boundary Discriminative Noise Detection”,

Journal of Information Process System, Vol.9,

No.1.

[11] Pawan Patidar and Manoj Gupta (2010),

“Image De-noising by Various Filters for Different

Noise”, International Journal of Computer

Applications (0975 – 8887) Volume 9– No.4,

November.

[12] P.E.Ng and K.K.Ma(2006),‟ Aswitching

median filter with boundary discriminative noise

detection for exteremely corrupted image,”IEEE

Trans. Image process, vol.15,no.6,PP.1506-1516.

[13] Priyanka Kamboj and Versha Rani (2013), “A

Brief Study Of Various Noise Model And Filtering

Techniques”, Journal of Global Research in

Computer Science, Volume 4, No. 4.

[14] Ravinder Singh Balwinder Singh and

Harmandeep Singh (2013), “Removal of High

Density Salt & Pepper Noise in Noisy color Images

using Proposed Median Filter” International

Journal of Advanced Research in Computer

Science and Electronics Engineering (IJARCSEE)

Volume 2, Issue 2.

[15] Sanjeev Kumar, Asutosh Kar and Mahesh

Chandra (2014), “SVM Based Adaptive Median

Filter Design for Face Detection in Noisy Images”,

International Conference on Signal Processing and

Integrated Networks (SPIN).

[16] S.Gopi Krishna, T. Sreenivasulu Reddy

andG.K.Rajini, “Removal of High Density Salt and

Pepper Noise Through Modified Decision Based

Unsymmetric Trimmed Median Filter”

International Journal of Engineering Research and


3239


Applications (IJERA) Vol. 2, Issue 1, Jan-Feb

2012.

[17] Srinivasan, K.S., Ebenezer, D. (2007), “A new

fast and efficient decision based algorithm for

removal of high-density impulse noises”, IEEE

Signal Process.Lett.14(3), Pp.189–192.

[18] Sucharita Padhi Debananda Padhi, and Bratat

iMohanty (2012), “Order Statistic Filters for

removing Salt-and-Pepper Noise of Images: A

Comparative Study” International Journal of

Advanced Research in Computer Science and

Software Engineering Volume 2, Issue 1.

[19] Sukomal Mehta, Sanjeev Dhull (2012), “fuzzy

based median filter for grey-scale images”,

International journal of engineering science &

advanced technology, ISSN:2250-3676,Volume-

2,Issue-4.

1.Author Profile

Ramandeep Kaur received the M.SC(IT) Master

Degrees in Information Technology degree from

T.P.D punjabi university nebhourhood campus

rampura-phul in 2012.Currently she is pursuing

m.tech degree in information technology from

Guru Kashi University, Talwandi Sabo, Bathinda

(Punjab). Her research interests include digital

image processing and Image Enhancement.

Documents

A Fast and Robust Hybridized Filter For Image De-Noisingijsetr.org/wp-content/uploads/2014/12/IJSETR-VOL-3... · Filter, Order Static Filter, SVM, PSNR, MAE. I. INTRODUCTION Images