An Image Enhancement Method for Noisy Image
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8/13/2019 An Image Enhancement Method for Noisy Image
http://slidepdf.com/reader/full/an-image-enhancement-method-for-noisy-image 1/4
978-1-4244-5858-5/10/$26.00 ©2010 IEEE ICALIP20101144
An Image Enhancement Method for Noisy Image
Chuanwei Sun
1,2
1School of Information
Science and Technology
East China Normal
University2School of Information
Science and Engineering
University of Jinan
Shanghai , China
No.500 Dongchuan Road,
Shanghai, China 200241
Hong LiuSchool of Information
Science and Technology
East China Normal
University
Shanghai , China
No.500 Dongchuan Road,
Shanghai, China 200241
Jingao LiuSchool of Information
Science and Technology
East China Normal
University
Shanghai , China
No.500 Dongchuan Road,
Shanghai, China 200241
Abstract
In this paper, image enhancement for noisy image
has been studied. A simple approach to enhancement
of noisy image data is presented. The proposed method
is based on a two steps system that adopts pre-
denosing step in order to prevent the noise increase
during the sharpening of the image. The proposed
method has better performance than available methods
in the enhancement of noisy images. Simulation results show that this method can effectively improve the effect
of sharpening the noisy image.
1. Introduction
In image acquisition and transmission, due to the
impact of other objective factors such as environmental
conditions, system noise, relative motion, and so on,there will produce a difference between the original
image and the resulting image. We must take some
measures for its improvement so as to obtain the realimage information for special purpose. A large number
of algorithms for image noise removing have been proposed [1]–[7].
Digital image enhancement and analysis have played,
and will continue to play, an important role in scientific,
industrial, and military application [6].The principalobjective of enhancement is to process an image so that
the result is more suitable than the original image for a
specific application [1]. The enhancement of noisy data,
however, is a very critical process because the
sharpening operation can significantly increase thenoise [8]. Traditional image enhancement methods
include spatial domain methods and frequency domain
methods. The classic linear unsharp masking is
implemented by passing a low-contrast image through alinear two-dimensional high-pass filter and then adding
a fraction of its output to the original [9]. The method
enlarges the noise in the process of enhancement.In this paper, a simple method for the enhancement
of noisy image is presented. The proposed approach
consists of two steps: pre-denoising step andenhancement step. In Section 2, image noise and de-
noising methods is described, the method of the simple
image enhancement technique is presented in Section 3.In Section 4, the experimental discussion is presented,
and the conclusions are given.
2. Image Noise and De-noising Methods
The following section describes image noise and
de-noising methods.
2.1. Image Noise
Image noise styles may be divided differentlyaccording to different criterion. The criterions include:
the causes of image noise’s generation, the shape of the
noise amplitude distribution over time, noise spectrumshape and the relationship between noise and signal,
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and so on. For example, image noise can be divided
into additive noise and multiplicative noise according
to the relationship between noise and signal. There aremany types of image noise. Such as additive noise,
multiplicative noise, salt and pepper noise, Gaussian
noise.
In the research processing of digital image, we canadd Gaussian noise, localvar noise, Poisson noise, salt
and pepper noise and speckle noise to the originalimage in the Matlab platform. The Gaussian noise is
Gaussian white noise with constant mean and variance.
The localvar noise is zero-mean Gaussian white noisewith an intensity-dependent variance.
Probably the most frequently occurring noise is
additive Gaussian noise[6]. The PDF of a Gaussian
random variable, z , is given by2( )
221
( )2
z
p z e
(1)
Where z represents gray level, is the mean of
average value of z , and is its standard deviation[1].Salt and pepper noise refers to a wide variety of
processes that result in the same basic imagedegradation: only a few pixels are noisy, but they are
very noisy [6]. The PDF of Salt and pepper noise is
given by
( )
0
a
b
P for z a
p z P for z b
otherwise
(2)
If b>a, gray-level b will appear as a light dot in theimage. Conversely, level a will appear like a dark dot
[1].
(a) (b)
(c)
Figure 1. the original image and the polluted
image(a) Original lena image,(b) Image
corrupted by salt & pepper noise with, (c)
Image corrupted by Gaussian noise
Figure 1(a) shows the original image. To visualize
the impact of different types of noise on the quality oforiginal image, figure. 1(b) and (c) show the corrupted
image by salt & pepper noise with noise density 0.1 andGaussian noise with mean 0 and variance 0.02,respectively.
2.2. De-noising Methods
For different types of noise, we can choose differentmethods of de-noising based on the noise
characteristics. In general, image noise reduction
methods can be carried out in both space domain and infrequency domain. The average method can be used to
reduce noise in spatial domain, while various forms of
low-pass filtering methods can be used to reduce noise
in the frequency domain. This section describes thetypical methods of noise removing, including both
spatial domain method and frequency domain method.Median filtering, as a nonlinear operation, is a
typical spatial method of reducing salt and pepper noise
in an image. A median filter is more effective thanconvolution when the goal is to simultaneously reduce
noise and preserve edges [10].
Image smoothing is realized in the frequencydomain by reducing a specified range of high frequency
components. The low-pass filter can remove high
frequency noise of an image in the frequency domain.Commonly used frequency domain low-pass filter
includes: Ideal Lowpass Filter (ILPF), GaussianLowpass Filter (BLPF), Butterworth Lowpass filter(BLPF). The transfer function of Gaussian lowpass
filters in two dimensions is given by2 2( , ) exp( ( , ) 2 ) H u v D u v (3)
Where ( , ) D u v is the distance from any point to
the origin of the Fourier transform is a measure of
the spread of the Gaussian curve.
Figure 2ashows the result of filtering the noise
image shown in Figure 1(b) with a median filter of size
33, and Figure 2 bshows the result of filtering
the noise image shown in Figure 1(c) with a lowpass
Gaussian filter of sig 40.
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(a) (b)
Figure 2. (a) De-noising image of Figure 1 (b),
and (b) De-noising image of Figure 1 (c)
3. Image Enhancement
The Image enhancement is a process of image preprocessing. The principal objective of enhancement
is to process an image so that the result is more suitablethan the original image for a specific application [1].
Image enhancement methods can be divided into two
categories: spatial domain methods and frequencydomain methods.
In general, single image enhancement method can
not meet the actual requirements in digital image processing. A method of image enhancement to achieve
a better visual effect for image enhancement was
proposed. We can perform de-noising of the imagefirst and then sharpen the image.
3.1. Spatial Domain Image Enhancement
Spatial domain Image enhancement methodsincludes: gray level transformations, histogram
processing, arithmetic/logic operations, basic spatial
filters, smoothing spatial filters and sharpening spatialfilters.
In this section we will discuss spatial sharpening
method application in the enhancement of noisy image.We will use linear unsharp masking filter to enhance
the noisy image and the de-noising image. The unsharp
masking filter has the effect of making edges and fine
detail in the image more crisp [10].Experiment 1, we enhance the image in figure 1(b)
and figure 2(a) using the unsharp filter. Figure 3 showsthe enhanced image of Figure 1(b) and Figure 2(a). The
results show that the enhanced images of pre-denoising
have better performance then that of the noisy ones.
(a) (b)
(c) (d)
Figure 3. Enhanced image using the unsharp
filter for (a) Figure 1 (b), (b) 2 (a), (c) Figure 1 (c)
and (d) Figure 2 (b)
3.2. Frequency Domain Image Enhancement
The Frequency domain image enhancement methodsconsist of smoothing frequency domain filters,
sharpening frequency domain filters and homomorphic
filtering.
Given the transfer function( , )h p H u v
of highpass
filter and the corresponding transfer function( , )
lp H u v
of lowpass filter, we can get the relation[1]
( , ) 1 ( , )hp lp H u v H u v (4)
High-frequency emphasis filter has the transfer
function
( , ) ( , )hfe lp H u v a bH u v (5)
Where a is the offset and b is the multiplier.
In this section we will use high-frequency emphasisfilter to enhance the noisy image and de-noising image
firstly, and then using histogram equalization to further
enhance the image.Experiment 2, we enhance the image in Figure 1(c)
and Figure 2(b) using the highpass filters. Figure 4
shows the enhanced image of Figure 1(c) and Figure2(b). The results show that the enhanced pre-denoising
images have higher contrast than the noisy ones and are
smoother than that of the noisy ones.
(a) (b)
(c) (d)
Figure 4. Enhanced image using the highpass
filter for (a) Figure 1 (b), (b) Figure 2 (a), (c)
Figure 1 (c) and (d) Figure 2 (b)
4. Discussion and Conclusion
The Image enhancement method for noisy image is
proposed and implemented in section 3. The image
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named Lena with a size of 256×256 have been
corrupted by two different types of noise model,
including Gaussian noise and salt & pepper noise. The
pre-denoising method of Figure 2 ais a median
filter in the spatial domain, and the pre-denoising
method of Figure 2 bis a low-pass filter in the
time domain. The pre-denoising effect of median filteron salt & pepper noise is better than low-pass filter on
Gaussian noise.Firstly, the noisy image was enhanced using the
unsharp filter in the spatial domain, and the results is
shown in Figure 3. Figure 3(a) is the enhanced imageof lena corrupted by salt & pepper noise and Figure
3(b) is the enhanced de-noised image shown in Figure 2
b. Figure 3(c) is the enhanced image of lena by
Gaussian noise and Figure 3(d) is the enhanced de-
noised image, which is shown in Figure 2 b. We
can see that the enhanced image of denoised image has
good visual effect than the enhanced image of noisy
image.Secondly, the noisy image was enhanced in the
frequency domain. Figure 4(a) and (c) is the result of
noisy image Figure 1(b) and (c) respectively. Figure4(b) and (d) is the result of noisy image Figure 2(a) and
(b) respectively. The result shows that the sharpening
effect of the de-noising image is better than the noisyone.
The experimental results show that the method
proposed in the paper is effective and robust tocommon digital image signal processing operations.
Especially, it receives high visual effect under signal
enhancement operations, such as sharpening, edge
enhancement, histogram equalization, and so on.The key to the method is to classify the noise type
and select the correct de-noising and enhancementapproaches according to different purpose. By using the
method proposed in the paper, we can get better image
visual effect of the noisy image. And the method canhelp us get special characteristic of an image which is
useful to us.
Acknowledgements
This work was supported by the Ministry and City
Cooperation Project of Science and Technology
Supporting Plan in Shanghai Science and TechnologyCommittee (Grant No.10DZ0581000).
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