International Journal of Application or Innovation in Engineering & Management (IJAIEM)
Web Site: www.ijaiem.org Email: editor@ijaiem.org
Volume 3, Issue 5, May 2014
ISSN 2319 - 4847
AN APPROACH FOR DENOISING THE
COLOR IMAGE USING HYBRID
WAVELETS
Mohd Awais Farooque1, Sulabha.V.Patil2, Jayant.S.Rohankar3
1
Student of M.Tech Department of CSE, TGPCET, Nagpur
2,3
M.Tech Department of CSE, TGPCET, Nagpur
ABSTRACT
Color image preprocessing and segmentation has been widely accepted as an important component of the image mining. Images
are often degraded by noises. Noise can occur during image capture, transmission, etc. Noise removal is an important task in
Image processing. In this paper, we have proposed the denoising concept. Several techniques for noise removal are well
established in color image processing. The nature of the noise removal problem depends on the type of the noise
corrupting the image. The method used for pre-processing the color image using hybrid wavelet based segmentation which has
the advantage of more efficiency, better quality and accuracy of image. The preprocessing method wavelet transforming has the
advantage of multi-resolution in both time domains as well as in frequency domain,. Wavelet denoising is a more successful kind
of application of wavelet transforming. The experiment has shown enhanced results produced by our proposed technique than the
previous approaches in practice.
In general the results of the noise removal have a strong influence on the quality of the image processing technique.
Denoising of image is very important and inverse problem of image processing which is useful in the areas of image mining,
image segmentation, pattern recognition and an important preprocessing technique to remove the noise from the naturally
corrupted image by the different types of noises. The wavelet techniques are very effective to remove the noise also use of its
capability to confine the power of a signal in little convert of energy values.
Key Words: Noises, Types of noise, Wavelets & Hybridization of wavelets.
1. Introduction
The original meaning of "noise" was and remains "unwanted signal"; unwanted electrical fluctuations in signals received
by AM radios caused audible acoustic noise ("static"). By analogy unwanted electrical fluctuations themselves came to be
known as "noise". Image noise is, of course, inaudible.
Image noise is random (not present in the object imaged) variation of brightness or color information in images, and is
usually an aspect of electronic noise. It can be 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. Image noise is an
undesirable by-product of image capture that adds spurious and extraneous information [1][2].
There are many different types of noises available in an image for example Gaussian Noise, Speckle Noise, Salt & Pepper
Noise etc and many different types of techniques like Chroma and luminance noise separation, Linear & Non Linear
Filters, Selection of wavelet from wavelet families for image denoising are available in image processing Color image
preprocessing and segmentation has been widely accepted as an important component of the image mining. The method
used for pre-processing the color image includes wavelet based segmentation which has the advantage of more efficiency,
better quality and accuracy of image [3][4].The preprocessing method wavelet transforming has the advantage of multiresolution in both time domains as well as in frequency domain, so it can be used to describe the partial characteristics for
both domains. Wavelet denoising is a more successful kind of application of wavelet transforming..One of another
method Hybridization of wavelets means merging of two wavelets are proposed in this paper.
2. DENOSING
Denosing is the process with which we reconstruct a signal from a noisy Denoising a image is very important and inverse
problem of image processing which is useful in the area of image mining, image segmentation, pattern recognition.
Image denoising forms the preprocessing steps in the field of photography, research, technology and medical science
Figure 1(a) NMR Spectrum
Volume 3, Issue 5, May 2014
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International Journal of Application or Innovation in Engineering & Management (IJAIEM)
Web Site: www.ijaiem.org Email: editor@ijaiem.org
Volume 3, Issue 5, May 2014
ISSN 2319 - 4847
Figure 1(b) Wavelet Shrinkage De-Noising
3. WAVELETS
A wavelet is a mathematical function useful in digital signal processing and image compression. The use of wavelets for
these purposes is a recent development, although the theory is not new. The principles are similar to those of Fourier
analysis, which was first developed in the early part of the 19th century.
A wavelet is a wave-like oscillation with amplitude that begins at zero, increases, and then decreases back to zero. It can
typically be visualized as a "brief oscillation" like one might see recorded by a seismograph or heart monitor. In signal
processing, wavelets make it possible to recover weak signals from noise. This has proven useful especially in the
processing of X-ray and magnetic-resonance images in medical applications. Images processed in this way can be
"cleaned up" without blurring or muddling the details [5][6].
There are many different types of wavelets like Daubechies wavelet; Haar wavelet, Meyer wavelet, Symlet wavelet etc are
available to denoise the image and to improve the image quality which has been affected by noise. The Wavelet technique
are very effective to remove the noise also because of its capability to confine the power of signal in little convert of
energy values. As a mathematical tool, wavelets can be used to extract information from many different kinds of data.
Wavelets can be combined, using a "reverse, shift, multiply and integrate" technique called convolution, with portions of
a known signal to extract information from the unknown signal.
The Need for Wavelets
The Wavelet transform performs a correlation analysis; therefore the output is expected to be maximal when the input
signal most resembles the mother wavelet. Often signals we wish to process are in time-domain, but in order to process
them more easily other information, such as frequency, is required. A good analogy for this idea is given by Hubbard. The
analogy cites the problem of multiplying two roman numerals in to our numbers system and then translates the answer
back into a roman numeral. The result is the same, but taking the detour into an alternative number system made the
process easier and quicker. Similarly we can take a detour into frequency space to analysis or process a signal. If a signal
has its energy concentrated in a small number of WL dimensions, its coefficients will be relatively large compared to any
other signal or noise that its energy spread over a large number of coefficients [12].
Multi resolution and Wavelets
The power of wavelets comes from the use of multi resolution. Rather than examining entire signals through the same
window, different parts of the wave are viewed through different size windows (or resolutions).High frequency parts of
the signal use a small window to give good time resolution low frequency parts use a big window to get good frequency
information [12].
An important thing to note is that the windows have equal area though the height and width may vary in wavelet
analysis. The area of the window is controlled by Heisenberg’s uncertainty principle, as frequency resolution gets bigger
the time resolution must get smaller.
Fig 2. The Different transform provided different resolutions of time and frequency
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International Journal of Application or Innovation in Engineering & Management (IJAIEM)
Web Site: www.ijaiem.org Email: editor@ijaiem.org
Volume 3, Issue 5, May 2014
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In Fourier analysis a signal is broken up into sine and cosine waves of different frequencies, and it effectively rewrites a
signal in terms of different sine and cosine waves. Wavelets analysis does a similar thing, it takes a mother wavelets then
the signal is translated into shifted and scale version of this mother wavelet.
Wavelets and Compression
Wavelets are useful for compressing signals but they also have far more extensive use. They can be used to process and
improves signal, in fields such as medical imaging where image degradation is not tolerated they are of particular use.
They can be used to remove noise in an image, for example if it is of very fine scale, wavelets can be used to cut out this
fine scale, effectively removing the noise [12].
Wavelet compression in MATLAB
MATLAB has two interfaces which can be used for compressing image, the command line and the GUI, Both interface
take an image decomposed to a specified level with a specified wavelets and calculate the amount of energy losses and
numbers of zero’s [12].
When compressing with orthogonal wavelets the energy retained is:
100*(vector-norm (coeff. of the current decomposition, 2)) 2 / (vector-norm (original signal, 2)) 2
The number of zeros in percentage is defined by:
100*(numbers of zeros of the current decomposition) / (Number of coefficients)
To change the energy retained and number of zero values, a threshold value is changes. The threshold is the number
below which detail coefficient are set to zero. The higher the threshold value, the more zeros can be set, but the more
energy is lost. Thresholding can be done globally or locally. Global thresholding involves thresholding every sub band
(sub-image) with the same threshold value. Local thresholding involves uses a different threshold value for each sub
band.
WAVELET FAMILIES FOR IMAGE DENOISING
In wavelet based image denoising the selection of wavelets is essential which decides the of image performance quality.
There are several great choices of wavelets which depend on the selection of wavelet function. Thus selection of wavelet
family depends on the size and resolution. Therefore, the best selection of wavelet depends on the particular images to be
denoised. In wavelet based image denoising, the (SNR) signal to noise ratio performance mostly dependent on selection of
wavelet. This paper analyses, various wavelet function families like Meyer, Daubechies, Coiflets, Symlets, Haar and
Biorthogonal [13] [14][10].
Fig 3. The Wavelet Families introduced in our study
Fig 3. Shows all the different waveforms which can be treated to indicate the complete parts of the image .Daubechies can
be seen as the star in the world of research of Wavelet which is called as compactly supported orthonormal Wavelets with
biorthogonality and six coefficients. The names of the Daubechies family wavelets are written dbN, where N is the order,
and db the surname of the wavelet. Haar wavelet is one of the simplest and old wavelet which has the compact
maintenance that means it disappears outer surface of a finite interval. Therefore the discussion of the wavelets is with
Haar which is discontinuous in nature and bears a resemblance to a step function [14].
The Daubechies, bior and Coiflets are compactly supported orthogonal Wavelets [14]. The Meyer wavelet families are
symmetric in shape. The choices of wavelets are based on their shape and their ability to denoise the image in a particular
environment and application. The Coiflet has 2M moments is equal to zero and the scaling function has 2M-1 moments is
equal to zero. Biorthogonal Wavelet family shows the property of linear phase, which is needed for image and signal
Volume 3, Issue 5, May 2014
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International Journal of Application or Innovation in Engineering & Management (IJAIEM)
Web Site: www.ijaiem.org Email: editor@ijaiem.org
Volume 3, Issue 5, May 2014
ISSN 2319 - 4847
reconstruction. By using two wavelet families, the properties can be derived on the basis of decomposition and
reconstruction in place of the same single one [7].
4. METHODOLOGY AND PROPOSED WORK
Load Original
Image
Convert the
Original image
into matrix form
Image
Segmentation
Get Original
Denoised Image
Add type of
Noise
Wavelets
Hybridization
Add Noise
Varience
Choose Threshold
Hard/Soft
In this Paper, a novel method of preprocessing using wavelets Hybridization Transform has been proposed to extend the
traditional gray level to achieve the color image segmentation, First an image is been loaded then noise is added to that
image any noise (Gaussian, Salt & Pepper, Impulse etc) after that variance is been added means quantity of noise and
then we will calculate SNR, PSNR, MSE, RMSE then thresholding is applied on preprocessed image. After applying
multiple thresholding, we will apply hybrid wavelets and denoise the color image and then segmentation is applied which
use it to segment the image. The complete procedure is given in above figure. In the imminent sections this process is
explained in detail. Input is given as color image. Each component of the color image, Red, Green and Blue is separated.
Then wavelet Hybridization is applied for preprocessing on each component and the result is we get a denoise image and
then will apply segmentation on the results .We combine the co-efficient of each component to make a color image. This
Hybrid wavelet transformed image is denoised and we get the segmented image.
5. CONCLUSION AND FUTURE SCOPE
In this paper we have studied about that how Images are often degraded by noises. What is a Noise in an image and
Different types of noises available in an image. The first phase of this paper is about the noises that are available in an
image and the different types of efficient technique to denoise that image or to remove the noise from an image. The
Second phase is about the different types of filters and wavelets available to remove the noise from and image wavelets
that can be used to improve the quality of a corrupted image. Last the methodology proposed that is hybridization of
wavelets used to denoise the color image and then segmentation is applied which is used to segment the image in to
different parts. There are many different areas in which we can improve on. Our area of improvement would be to
develop a better optimality criterion. Further we will do segmentation after applying wavelets as image segmentation is
the front-stage processing of image compression. We hope that there are advantages in image segmentation for good
shape matching and better result generation. Besides this, we can introduce many segmenting methods including
threshold technique, data clustering, region growing, region merging and splitting, mean shift, and watershed.
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International Journal of Application or Innovation in Engineering & Management (IJAIEM)
Web Site: www.ijaiem.org Email: editor@ijaiem.org
Volume 3, Issue 5, May 2014
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