Comparative Study on Fuzzy Logic Based Mixed Noise Filter ...ijiset.com/v1s7/IJISET_V1_I7_17.pdf ·...

IJISET - International Journal of Innovative Science, Engineering & Technology, Vol. 1 Issue 7, September 2014.

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Comparative Study on Fuzzy Logic Based Mixed Noise Filter for Colour Images

P

1PM.Syed Mohamed Ibrahim, P

2PS.Abdul Saleem,

1M.phil scholar, 2Assistant professor, P

1,2PPG & Research Dept.of comp.science,

Jamal Mohamed college(Autonomous) Trichirapalli-620 020,

ABSTRACT

The objective of this project is to denoise the color images. This project denoises the images which are only affected by the Gaussian, Impulse and mixed Gaussian-impulse noise. In this project a new filter is used to remove the noise. This filter uses the fuzzy peer group concept to remove the noise. The fuzzy peer group extends the peer group concept in the fuzzy setting. A fuzzy peer group will be defined as a fuzzy set that takes a peer group as support set. The fuzzy peer group of each image pixel will be determined by means of a novel fuzzy logic-based procedure. The fuzzy peer group concept is used to design a two-step color image filter. Cascading a fuzzy rule-based filter which is designed to remove the impulsive noise. Fuzzy average filtering is designed to remove the Gaussian noise. The performance of this filter is compared to the fuzzy logic based adaptive filter noise filter. Peak-Signal-to-Noise-Ratio, Mean Square Error and Root Mean Square Error metric are used to measure the performance of the denoising filter

1. INTRODUCTION

A technique in which the data from an image are digitized and various mathematical operations are applied to the data, generally with a digital computer, in order to create an enhanced image that is more useful or pleasing to a human observer, or to perform some of the

interpretation and recognition tasks usually performed by humans, also known as picture processing. Manipulating data in the form of an image through several possible techniques is known as image processing. An image is usually interpreted as a two-dimensional array of brightness values, and is most familiarly represented by such patterns as those of a photographic print, slide, television screen, or movie screen. An image can be processed optically or digitally with a computer.To digitally process an image, it is first necessary to reduce the image to a series of numbers that can be manipulated by the computer. Each number representing the brightness value of the image at a particular location is called a picture element, or pixel. A typical digitized image may have 512 × 512 or roughly 250,000 pixels, although much larger images are becoming common. Once the image has been digitized, there are three basic operations that can be performed on it in the computer. For a point operation, a pixel value in the output image depends on a single pixel value in the input image. For local operations, several neighboring pixels in the input image determine the value of an output image pixel. In a global operation, all of the input image pixels contribute to an output image pixel value. These operations, taken singly or in combination, are the means by which the image is enhanced, restored, or compressed. Image processing is an active area of research in such diverse fields as medicine, astronomy, microscopy, seismology, defense, industrial quality control, and the publication and

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entertainment industries. The concept of an image has expanded to include three-dimensional data sets (volume images), and even four-dimensional volume-time data sets. An example of the latter is a volume image of a beating heart, obtainable with x-ray computed tomography (CT). CT, PET, single-photon emission computed tomography (SPECT), MRI, ultrasound, SAR, co focal microscopy, scanning tunneling microscopy, atomic force microscopy, and other modalities have been developed to provide digitized images directly. Digital images are widely available from the Internet, CD-ROMs, and inexpensive charge-coupled-device (CCD) cameras, scanners, and frame grabbers. Software for manipulating images is also widely available. 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 is enhanced when it is modified so that the information it contains is more clearly evident, but enhancement can also include making the image more visually appealing. An example is noise smoothing. To smooth a noisy image, median filtering can be applied with a 3 × 3 pixel window. This means that the value of every pixel in the noisy image is recorded, along with the values of its nearest eight neighbors. These

nine numbers are then ordered according to size, and the median is selected as the value for the pixel in the new image. As the 3 × 3 window is moved one pixel at a time across the noisy image, the filtered image is formed.

Denoising is the one of the pre processing technique in digital image processing. Digital images are often corrupted by noise during their acquisition and transmission. Noise is also introduced in digital images when scanning damaged surfaces of the originals. The noise in the digital image is removed by the denoising technique. It is the first step of the most image processing techniques. Several denoising methods are available. It gives confusion for select the best filters. To avoid the confusion this project compares the three techniques and gives the final best filters. This is the motivation of this project. The objective of this project is to denoise the color images. There are several types of noises are occurred. But this project denoises the images which are only affected by the Gaussian, Impulse and mixed Gaussian-impulse noise. Several filters are used to reduce the noises. This project investigates three denoising methods. They are Adaptive Bilateral Filter (ABF), Fuzzy Peer Group Filter and Switching Bilateral Filter. The performance of the above three filters are compared by using three performance metrics. They are Peak-Signal-to-Noise-Ratio, Mean Square Error and Root Mean Square Error.

In terms of noise removal, conventional linear filters work well for removing additive Gaussian noise, but they also significantly blur the edge structures of an image. Therefore, a great deal of research has been done on edge-preserving noise reduction. One of the major endeavors in this area has been to utilize rank order information [2,26,15,36]. Due to a lack of the sense of spatial ordering, rank order filters generally do

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not retain the frequency selective properties of the linear filters and do not suppress Gaussian noise optimally [42]. Hybrid schemes combining both rank order filtering and linear filtering have been proposed in order to take advantage of both approaches [43], [25]. These nonlinear rank order approaches in general improve the edge sharpness, but they are more complex to implement than a spatial linear filter. In more recent years, a new concept in edge-preserving de-noising was proposed by Smith and Brady [40] and Tomasi and Manduchi [45]. Although their algorithms were developed independently, and named the “SUSAN” filter and the “bilateral filter,” respectively, the essential idea is the same: enforcing both geometric closeness in the spatial domain and gray value similarity in the range in the de-noising operation. The idea of bilateral filtering has since found its way into many applications not only in the area of image de-noising, but also computer graphics [9], [10], video processing [11], [12], image interpolation [13], illumination estimation [14], as well as relighting and texture manipulation, dynamic range compression, and several others pointed out in [15]. Several researchers have provided a theoretical analysis of the bilateral filter and connected it with the classical approaches to noise removal. Elad [16] demonstrated that the bilateral filter emerges from the well-known Bayesian approach [17], [18], when a novel penalty function is used. Based on this observation, he proposed methods to speed up the bilateral filtering and to implement a bilateral filter for piece-wise linear signals. In [14], Elad also pointed out that the bilateral filter is a discrete version of the short-time effective kernel of the Beltrami flow discussed in [19] and [20]. Barash and Comaniciu [21], [22] demonstrated that the nature of the bilateral filter resembles that of anisotropic diffusion [34], [6], and outlined a common framework for bilateral filtering, nonlinear diffusion, adaptive smoothing [38], and a

mean shift procedure [26]. A good review on bilateral filter, its properties, and applications can be found in [15]. The bilateral filter is essentially a smoothing filter but it does not restore the sharpness of a degraded image. Most filters for Gaussian noise suppression are designed to take advantage of the zero-mean property of the noise and try to suppress it by locally averaging pixel channel values. Classical linear filters, such as the Arithmetic Mean Filter or the Gaussian Filter [27], smooth noise but blur edges significantly. To approach this problem, many nonlinear methods have been recently proposed, for instance: the bilateral filter [30], [31], the anisotropic diffusion [33], the chromatic filter [34], or the soft switching methods in [35] and [36], which motivate other fuzzy methods as the fuzzy directional derivative filter [37], the fuzzy bilateral filter [38], the fuzzy noise reduction method [39], or the fuzzy-switching filter [40]. The aim of these methods is to detect edges and details by means of local statistics and smooth them less than the rest of the image to better preserve their sharpness. However, these methods commonly identify impulses as details or edges to be preserved, and, therefore, they are not able to reduce them. On the other hand, it is noted that earlier filters for impulse noise are based on the theory of robust statistics because impulses are identified with outlier data, and, therefore, robust statistics allow us to appropriately determine noise-free samples and remove outliers. Filters of this family are, for instance, the popular median filter [47], the vector median filter [3], the vector directional filter [49], the directional-distance filter [36], and the HSV vector median filter [18], among others [29], [28], [7]. These filters are efficient in reducing impulse noise but their signal-preserving capability is deficient because the filtering operation is applied to each image pixel regardless whether it is noisy or not. To overcome this drawback, several adaptive filters have

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been recently introduced. These filters may be classified into the following categories: switching filters, filters using weighting coefficients, fuzzy filters, and neuro-fuzzy filters. However, many of these techniques select the appropriate noise-free output from the input samples, and, therefore, they are not useful to remove Gaussian noise because in such a case there are no noise-free samples. According to the above, the filter design is a challenging task for mixed Gaussian-impulse noise removal. A possible solution is to apply two consecutive filters to remove first impulse noise and then Gaussian noise, or vice versa. However, the application of two filters could dramatically decrease the computational efficiency of the method which implies that this solution could not be practical for real applications. Therefore, it is more interesting to devise specific filters to remove mixed noise. To date, a few methods in the literature are able to approach this problem efficiently. The Peer Group Averaging (PGA) technique presented in [9]–[22] removes mixed noise by combining a statistical method for impulse noise detection and replacement with an averaging operation to smooth out Gaussian noise.

2 .Methodology:

In this the modules of the existing system and the proposed system are described briefly.

2.1 Existing System

2.1.1 Modules

Choose Original Image Apply Noise Apply Median Filter Apply Fuzzy Logic Median Filter

of 3x2 Apply Fuzzy Logic Median Filter

of 4x2 Apply Histogram

Fig 2.1 Block Diagram of Noise Detection & Reduction Process

2.1.2 Modules Description

Choose Original Image

Original Pixel

Construction of Fuzzy Peer Group

Fuzzy rule Impulsive

Noise Detection

Fuzzy Averaging

Noise Free Pixel

Start

End

No Impulsive

Impulsive

Apply Vector M Filter

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o In choosing the original image, the gray scale image read by the MATLAB software.

Apply Noise In order to test the performance rate of this proposed algorithm experiments are Performed at different noise levels ranging from 5% to 40% on two types of images.

Apply Median Filter In this step 3x3 matrix scanning fuzzy logic based median filtering. The neighbouring pixels are ranked according to brightness (intensity) and the median value becomes the new value for the central pixel. Median filters can do an excellent job of rejecting certain types of noise, in particular, “shot” or impulse noise in which some individual pixels have extreme values. In this filtering operation, the pixel values in the neighbourhood window are ranked according to intensity and the middle value (the median) become the output value for the pixel under evaluation. so the process is apply to the 3x3 matrix scanning fuzzy logic based median filter to the noisy gray scale image. It removes the noise from the previously generated noisy image.

Apply Fuzzy Logic Median Filter of 3x2

In the step 4 3x2 matrix scanning fuzzy logic based median filter to the noisy gray scale image. Process is same as in the previous step but in the previous 3x3 scan matrix has some disadvantage that it remove the noise from the centre of the image but some kind of noise is remain on the edges of the image. So to remove the noise from the edges of image 3x2 matrix scanning is used.

Apply Fuzzy Logic Median Filter of 4x2

Again to improve the image quality 4x2 matrix scanning fuzzy logic based median filter is used and create the histogram also. Histogram is creating to show the

remaining noise in the image. Same process is repeated again for the 4x4 matrix scanning for the removal of noise by using fuzzy based median filter.

Apply Histogram Remaining noise is removed from the image by creating histogram of the image.

2.2 Proposed System

2.2.1 Modules

Choose Original Image Apply Noise Apply Fuzzy Peer Group Noise Reduction based on Fuzzy

Peer group Apply Histogram

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Fig 2.2 Block Diagram of Fuzzy Peer Group Construction

2.2.2 Modules Description

Choose Original Image In choosing the original image, the gray scale image read by the MATLAB software.

Apply Noise n order to test the performance rate of this proposed algorithm experiments are performed at mixed noise levels ranging from 5% to 40% on two types of images.

Apply Fuzzy Peer Group In fuzzy peer group the similarity between each of the pixel in the input image is calculated and the member of the pixel is taken based on the fuzzy similarity and the neighbors of the input pixels. Apply the below steps for processing all pixels in the input image. Get the input pixels. Find the neighbors of the input pixel. Find the fuzzy similarity for the input pixel. Find the Best Member of that pixel based on the fuzzy similarity

Noise Reduction based on Fuzzy Peer group

In noise reduction based on fuzzy group, the noises are gradually reduced by the fuzzy peer group members which is created in the previous step. To do this, the pixel members are taken and the following the fuzzy rule is applied on the input pixels which is as follows. 1.Apply the Fuzzy Rule 1 on the Best members of the input pixels

2.If the rule 1 is satisfied that pixel is considered as the salt & pepper noise pixel

Original Pixel Fi

Get the Neighboring Pixels Fj

Calculate Fuzzy Similarity ρ(Fi,Fj)

Find the accumulated

similarity

Find

Form the Fuzzy Rule1

If

CFR1(m) >0.1

Yes

Best Members

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3.Then apply vector median filter on the pixel 4.Otherwise it is considered as the Gaussian pixel 5.Then apply the smoothing procedure for remove the Gaussian pixel 6.Repeat the above steps for all input pixels 7.Finally we get the noise free image Apply Histogram

Remaining noise is removed from the image by creating histogram of the image.

3.COMPARATIVE STUDY

In comparative study, both the existing and the proposed work are combined to study the difference between them. For this, the existing and the proposed work are experimented by various images and it is evaluated by using various performance metrics which is as follows. 3.1 EXPERIMENTAL ANALYSIS In experimental analysis the existing system and the proposed system are experimented with various standard input images. 3.1.1 EXISTING SYSTEM In order to evaluate the working of the existing of the system, it is processed with various input images and the results are shown. 3.1.1.1 Experimental Images

The standard input images given are shown below.

(a) (b)

(c)

(d)

Fig. 3.1 Various Input Images (a) Boat (b) Cameraman (c) Lena (d) Peppers

3.1.1.2 Experimental Results The existing system is

implemented using MATLAB program where it is evaluated for denoising the noised image.

Fig. 3.2 Original Image

Fig. 3.3 Noise Applied Image

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Fig. 3.4 Median Filtered Applied Image

Fig. 3.5 Fuzzy Logic Median Filter 3x2

Fig. 3.6 Fuzzy Logic Median Filter 4x2

Fig. 3.7 Histogram of Denoised Image

3.1.2 PROPOSED SYSTEM The proposed method uses the

concept of fuzzy peer group for a colour image pixel which extends the concept of peer group in the fuzzy setting. This concept aims to represent the set of all pixel neighbours to a given pixel which are similar to it. Since the similarity between colour pixels is an imprecise concept, this method has represented it using fuzzy similarities. Thus, fuzzy peer groups are built as fuzzy sets where the membership degree of the neighbour pixels depends on their fuzzy similarity with respect to the pixel under processing. In order to evaluate the working of the proposed the system, it is processed with various input images and the results are shown.

3.1.2.1 Experimental Images

The standard input images given are shown below.

(a)

(b)

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(c)

(d) Fig. 3.8 Various Colour Input Images (a) Boat (b) Cameraman (c) Lena (d) Peppers

3.1.2.2 Experimental Results

The existing system is implemented using MATLAB program where it is evaluated for denoising the noised image.

Fig. 3.9 Original Image

Fig. 3.10 Noise Applied Image

Fig. 3.11 Apply Noise Reduction based on

Fuzzy Peer Group

Fig. 3.12 Histogram of Denoised Image

3.2 PERFORMANCE METRICS There are various performance metrics available to evaluate the performance of the project. In our project, we use the peak signal to noise ratio (PSNR), mean squared error rate (MSE) and root mean squared error rate (RMSE). The performance formulas are shown below.

3.2.1 Peak Signal-to-Noise Ratio (PSNR)

The peak signal-to-noise ratio (PSNR) is used to evaluate the quality between the denoised image and the original image. The PSNR formula is defined as follows:

𝑃𝑆𝑁𝑅

= 10 𝑋 𝑙𝑜𝑔10 255 𝑋 255

1𝐻𝑋𝑊∑ ∑ [𝑓(𝑥,𝒴) − 𝑔(𝑥,𝒴)]2𝑊−1

𝒴=0𝐻−1𝑥=0

𝑑𝐵

Where H and W are the height and width of the image, respectively; and f(x,y) and g(x,y) are the grey levels located at

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coordinate (x,y) of the original image and denoised image, respectively.

3.2.2 Mean Squared Error Rate (MSE)

The mean square error or MSE of an estimator is one of many ways to quantify the difference between an estimator and the true value of the quantity being estimated. As a loss function, MSE is called squared error loss.

𝑀𝑆𝐸 = 1𝑛

�(𝑌𝚤� − 𝑌𝑖)2𝑛

𝑖=1

Where 𝑌𝚤� is the vector of n predictions and 𝑌𝑖 is the vector of true values.

3.2.3 Root Mean Squared Error Rate (MSE)

The RMSE is frequently used to measure the difference between values predicted by a model or an estimator and the values actually observed. It the square root of the mean squared root error value.

𝑅𝑀𝑆𝐸 = �1𝑛

�(𝑌𝚤� − 𝑌𝑖)2𝑛

𝑖=1

Where 𝑌𝚤� is the vector of n predictions and 𝑌𝑖 is the vector of true values.

3.3 PERFORMANCE EVALUATION In order to evaluate the performance metrics, it is plotted in the table to identify the values of the proposed system. The tables are shown below. Table 3.3.1 PSNR Values for Existing and

Proposed Work Images Existing Proposed Boat 32.6078 35.2014

Camera 33.7908 34.2513 Lena 32.8650 34.5261

Barbara 33.2874 35.2614 Pepper 32.8547 34.5216

Table 3.3.2 MSE Values for Existing and


Camera 5.2124 2.8547 Lena 5.7982 3.1245

Barbara 5.9846 2.8645 Pepper 5.2346 2.1254

Table 3.3.3 RMSE Values for Existing and


Camera 2.2831 1.6896 Lena 2.4079 1.7676

Barbara 2.4463 1.6925 Pepper 2.2879 1.4579

To evaluate the performance metrics of the proposed system in an efficient way, it is plotted in the graph, in which it shows the difference in a graphical way. The graphs are shown below.

Table 3.13 PSNR Values for Existing and

Proposed Work

Table 3.14 MSE Values for Existing and

Proposed Work

30323436

PSN

R Va

lues

Various Images

Existing

Proposed

0

5

10

MSE

Val

ues

Various Images

Existing

Proposed

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Table 3.15 RMSE Values for Existing and

Proposed Work 4.CONCLUSION In this work, the removing of the noise is done by using the median filters in the existing work and with fuzzy peer group in the proposed work. To denoise the image from the noisy image, first the Gaussian noise is applied on the image and then it is denoised using the two techniques. In existing the median filter is applied by changing the filter size with various techniques. The noise removed by the median filter is not good enough. In proposed work, the fuzzy peer group is used. It extends the peer group concept in fuzzy setting. By this, the mixed noise is filtered. The noise removed by the fuzzy peer group is better than the existing work. In existing work, only the grayscale images are denoised. But in proposed work, the colour images also can be used. From the experimental results and comparative study, the proposed work shows better results. 5. FUTURE ENHANCEMENT

In future, this system can be used to remove noise in video. In this, we used vector methods to remove the noises, in future this technique can be enhanced by latest methods. The peek signal to noise ratio will be improved in the future. REFERENCES

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