An Image Enhancement Method for Noisy Image

8/13/2019 An Image Enhancement Method for Noisy Image

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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

[email protected]

Hong LiuSchool of Information


East China Normal

University

Shanghai , China



[email protected]

Jingao LiuSchool of Information


East China Normal

University

Shanghai , China



[email protected]

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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An Image Enhancement Method for Noisy Image