=Paper=
{{Paper
|id=Vol-2403/paper8
|storemode=property
|title=Noise Filtration in the Digital Images Using Fuzzy Sets
and Fuzzy Logic
|pdfUrl=https://ceur-ws.org/Vol-2403/paper8.pdf
|volume=Vol-2403
|authors=Yuliia Pomanysochka,Yuriy Kondratenko,Ievgen Sidenko
|dblpUrl=https://dblp.org/rec/conf/icteri/PomanysochkaKS19
}}
==Noise Filtration in the Digital Images Using Fuzzy Sets
and Fuzzy Logic==
https://ceur-ws.org/Vol-2403/paper8.pdf
Noise Filtration in the Digital Images Using Fuzzy Sets
and Fuzzy Logic
Yuliia Pomanysochka[0000-0003-3868-7621], Yuriy Kondratenko[0000-0001-7736-883X],
Ievgen Sidenko[0000-0001-6496-2469]
Intelligent Information Systems Department, Petro Mohyla Black Sea National University,
68th Desantnykiv Str., 10, Mykolaiv, 54003, Ukraine,
jul.pom19@gmail.com, yuriy.kondratenko@chmnu.edu.ua,
ievgen.sidenko@chmnu.edu.ua
Abstract. In this paper, existing methods for filtering noise in digital images
are considered. The following noise filtration methods were analyzed: arithme-
tic averaging filter, geometric averaging filter, median filtering, adaptive medi-
an filtration, Gaussian filtration and filtration using fuzzy logic, in particular the
fuzzy color preserving Gaussian noise reduction method (FCG filter). Besides,
the different types of noise that may occur on a digital image are discussed. All
methods were evaluated using metrics like mean squared error, peak signal-to-
noise ratio and structure similarity. It has been found that all of the above meth-
ods can well filter out only a certain type of noise. Pulse noise on a digital im-
age better removed with median and adaptive median filtering. Gaussian noise
better removed with averaging, Gaussian and FGG filters. In this paper, a com-
bination of adaptive median filtering and FGG filter is proposed for removal of
combined pulse and Gaussian noises.
Keywords: digital image, filtering noise, fuzzy set, fuzzy logic, mean squared
error, peak signal-to-noise ratio, structure similarity.
1 Introduction
Nowadays, almost all of the images are presented in the digital form. They are used in
printing, media, medicine, industry, space industry and other areas. Therefore, algo-
rithms and methods for their processing are rapidly developing and demand constant
improvements [1-3].
Processing of digital imaging is any change in the data, which is presented in the
form of digital images, in order to improve their visual perception by people (for ex-
ample, correcting color and contrast, correcting small noise) or further processing by
information systems (for example, segmentation to the area of certain classes, selec-
tion of objects, etc.) [1].
One of the main tasks of digital image processing is to remove noise that may oc-
cur while receiving images, transferring them or as a result of data digitization. The
process of eliminating various types of noise from images is called filtration [2, 4].
This work is devoted to solving this problem. Both classical filters and those built
on the basis of fuzzy logic are considered. The result of the study is a combination of
several filters in order to reduce combined noise from digital images.
2 Related Works and Problem Statement
The task of processing images using fuzzy logic techniques was expressed by scien-
tists from the 1990s [1-4]. Initially, research was conducted to create filters for black
and white images, then color images, and in recent years there have been develop-
ments for filtration of video frames with Fuzzy Logic Methods (FLM) for both black
and white and color frames. Most studies focus on two types of noise: impulse (ran-
dom noise) and Gaussian additive.
The GOA (Gaussian noise reduction) filter [5] reduces the Gaussian noise from
the black and white image and uses the fuzzy rules for determination the degree to
which the gradient in a certain direction is small (the idea is that a small gradient is
caused by noise, while a large gradient is caused by image structure). Fuzzy rules [6]
are also used for calculation the value of correction that is used for performing filtra-
tion (the contribution of neighborhood pixels depends on their gradient values).
Another filter for black and white images is called FuzzyShrink [7]. It represents
the modification of Wavelet filters using FLM. It showed better results than previous-
ly created fuzzy filters.
Also for removal impulse noise from black and white digital images FIDRM
(Fuzzy Impulse noise Detection and Reduction Method) filter was developed [8]. It
uses a similar approach as in the filter GOA, because it also uses a gradient values for
denoising the images.
The FRINR (Fuzzy Random Impulse Noise Reduction) filter also eliminated ran-
dom impulse noise on grayscale images [9]. The detection of noise in FRINR consists
of two stages. Firstly, the neighborhood around the pixel is investigated to determine
whether the pixel can be regarded as an impulse noise. If so, then fuzzy gradient val-
ues are used to determine the degree to which the pixel can be considered as an im-
pulse noise and the degree to which the pixel can be considered free of noise.
Subsequently, FIDRMC (Fuzzy Impulse noise Detection and Reduction Method
for Color images) and HFRMC (Histogram-based Fuzzy Restoration Method for Col-
or Images) filters were developed [10-11]. They focus on removing impulse noise
from color images. FIDRMC consists of two phases: the phase of detecting noise and
the phase of proper filtration. At the filtering stage, information about the color of a
particular neighborhood around the given central pixel is also taken into account.
Next, a Fuzzy Color preserving Gaussian noise reduction method (FCG) [12] was
developed to remove Gaussian noise on color digital images. Unlike most other exist-
ing methods, the first FCG subfilter distinguishes between deviations in pixel values
due to noise from those that are determined by the structures in the image (object
boundaries), using the distances between the color components instead of calculating
the difference between them.
3 Basic Concepts and Methods of Digital Image Filtering
In the digital image processing, it is assumed that the images represent an N M
integer table, where the value of each element corresponds to a certain level of bright-
ness. This is the so-called pixel coordinate system [3, 4].
Digital images are generally divided into two classes: vector and raster. Vector
image is an image, which is described as a set of graphic primitives. It is drawn by
lines on graphic output devices. Raster image is a two-dimensional array and its ele-
ments contain color information. It is targeted for bitmap display devices. Noise re-
moval methods work with raster images, so we will not consider the vector ones [2].
Impulse noise is modeled as follows. The appearance of noise emissions in each
pixel i, j has the probability p and does not depend on the presence of noise in
other points or the quality of the image. The pixel brightness value is replaced by the
new value d (from 0 to 255). Let xi , j will be a distorted image. Then
d with probability p
xi , j , (1)
si , j with probability 1 p
where si , j is the output brightness of the pixel i, j .
If the new value d 0 , then the black values of brightness (pepper type noise) are
added, if d 255 then the white values of brightness (noise type "salt").
Additive noise is described as
g x, y f x, y x , y , (2)
where f x, y is an input image; g x, y is a noised image; x, y is an additive
and independent noise with Gaussian or other distribution of probability density func-
tion.
Gaussian noise (also called normal noise) occurs on the image as a result of the
factors such as noise in electrical circuits, noise of sensors (due to lack of lighting or
high temperature). The model of this noise is widely used in the filtration of images
and signals [1].
The general principle of image filtering.
Noise reduction is achieved by filtration. The variety of image filtration methods
is associated with a variety of mathematical models of signals, noise and filtering
optimality criteria. The filtration is carried out in spatial or frequency domains. In the
frequency domain, the image must be converted into a frequency representation, for
example, by using Fourier transform [13, 14].
All image processing methods discussed in this paper are implemented in a spatial
area that is simply a plane containing image pixels. Spatial methods operate directly
by pixels of the image, on the opposite of frequency methods, in which operations are
performed over the results of the Fourier transform of the image, and not on the image
itself. Typically, spatial methods in a computational sense are more efficient and re-
quire less computing resources when implemented [3].
The processed (filtered) image is retrieved during the process of scanning the orig-
inal image by a filter. If the operator T, executed above the pixels of the noised image,
is linear, then the filter is called a linear spatial filter. Otherwise, the filter is nonlinear
[14].
Let's consider the basic variants of low-frequency filters. They are implemented
by linear operations [15, 16].
A large group of low-frequency filters are averaging (or smoothing) filters. In
such filters, a different way of calculation the average brightness value in a window
may be applied. Consider the arithmetic and geometric averaging filters.
The arithmetic averaging filter, or “box-box” filter, averages the value of the
brightness of the pixel around the neighborhood using a mask with the same coeffi-
cients, for example, for a mask size 3x3, the coefficients are 1/9, for 5x5 – 1/25 [17].
With geometric averaging, there is a smoothing of an image similar to arithmetic
averaging. Such a filter causes a deterioration of the sharpness that is characteristic of
all filters in this class, but some objects of the original image are less distorted. This
filter, as well as the averaging arithmetic, can be used to suppress the high-frequency
additive noise [1, 3].
Gauss filter. When defining filters, you can use masks with different weights. It is
logical to assume that pixels located closer to the analyzed pixel have a greater effect
on the brightness that is calculated during the filtration process. One of the filter that
takes into account this fact is the Gaussian filter [6].
Low-frequency filtration methods lead to smoothing the image. They are linear
and optimal when removing noise that has a Gaussian distribution. On real images in
the boundaries of different objects, the brightness distribution has a different look.
Median filtering. Noises in the form of white or black dots are impulse-type
noise. Linear filters do not eliminate them completely, but only locally averaged their
values. Noises of this type are removed using non-linear filters, such as the median [2,
4, 5].
A separate class of nonlinear filters for removing noise from a digital image con-
sists of filters based on fuzzy logic techniques. Its general idea is averaging the pixel
value using the values of neighborhood pixels, taking into account such important
structures in the image as the boundaries of the objects and the color component,
which the filter should not distort [18-21].
The main problem that this filter solves is that it allows you to distinguish between
noise and boundaries of objects in the image, both of which represent a significant
change in pixel values. This is possible due to the fact that it calculates the 2-D dis-
tance between the various color components. For example, to filter a red component
in position i, j , the distance between the red and green and red and blue compo-
nents of some pixel window with the center of i, j is used, instead of calculating
the average pixel value only by using values from the same red color component [4].
The idea of these simple fuzzy rules [22-24] is to assign large weights to the
neighbors of the central pixel of windows that have the same color component as the
central pixel itself. The distance between two pairs is calculated using the Euclidean
distance.
Methods for evaluating the quality of the filtration.
The quality of the filtering is usually performed by comparing the original image
(without noise) with noised one, and then with the denoised one. In this way, you can
see how the image characteristics were improved after applying the filter [25-28].
The metrics of evaluation are the following criteria: MSE (Mean Square Error);
PSNR (Peak Signal to Noise Ratio); SSIM (Structural Similarity Image Measure-
ment).
The most universal criterion is MSE, which is determined by the formula:
1 M N
vi , j vi , j ,
2
MSE (3)
M N i 1 j 1
where vi , j is a pixel intensity i, j of the ideal (original) image without noise; vi , j is a
pixel intensity i, j of the denoised image.
The smaller the value of MSE (that is, the smaller the processed image differs
from the ideal one) the better [28].
The next criterion is the PSNR, which is determined using MSE:
L
PSNR 10 lg max , (4)
MSE
where Lmax is a maximum intensity level in the image.
Also widespread is the measure of structural similarity of images, proposed by
Wang [29].
4 Implementation of the Described Filters and the Combined
Filter
Authors implemented the filters described earlier and compared the results of their
work using image quality filtering criteria such as MSE, PSNR and SSIM. The results
are presented in the Tables 1-3 and Fig. 1-2.
Table 1. Comparison of filters for Gaussian noise filtration
MSE PSNR SSIM
Noised image 605,063 20,313 0,628
Arithmetic Averaging Filter 335,33 22,879 0,820
Geometric averaging filter 661,538 19,925 0,773
Gaussian filter 292,768 23,466 0,762
FCG filter 234,341 24,432 0,846
Median filter 574,6146 20,5370 0,6490
Thus, the best filter for the removal of Gaussian additive noise is the FGG filter.
(a) (b)
Fig. 1. Input noisy image (a) and result of image processing by the FCG filter (b)
Table 2. Comparison of filters for impulse noise filtering
MSE PSNR SSIM
Noised image 1055,659 17,896 0,562
Arithmetic Averaging Filter 401,3021 22,0961 0,7773
Geometric averaging filter 6246,1072 10,1747 0,2394
Gaussian filter 488,2479 21,2444 0,6948
FCG filter 571,9151 20,5575 0,6696
Median filter 207,3446 24,9639 0,9248
So, the best filter to remove impulse noise is the median filter.
It should be noted that more often on images there is a combination of several
noises, specifically Gaussian additive and impulse noises. We checked the efficiency
of the methods for filtering such combined noise.
Table 3. Comparison of filters for filtering of combined noise
MSE PSNR SSIM
Noised image 1613,751 16,052 0,446
Arithmetic Averaging Filter 483,2866 21,2888 0,7225
Geometric averaging filter 6460,1289 10,0284 0,2208
Gaussian filter 735,2863 19,4662 0,5941
FCG filter 535,3383 20,8445 0,6739
Median filter 586,7834 20,4460 0,6512
(a) (b)
Fig. 2. Input noisy image (a) and result of image processing by median filter (b)
Consequently, we can see that the above filters poorly remove the combined noise
from the images. The best result is shown by averaging arifmethic filter, but it also is
unsatisfactory.
Therefore, it is necessary to develop a tool for the removal of the combined type
of noise. To remove impulse noise, a median filter will be used, for the Gaussian
noise – filter FCG, which has been experimentally shown to be better than other filtra-
tion methods.
This should be done using two approaches: sequential applying of the above fil-
ters; combination of both methods in one adaptive filter [30-33].
The combined adaptive filter will work according to the following algorithm.
1. Create three windows individually for components R, G and B.
2. Checking the central pixels in each window:
calculating the average intensity of the window;
if the central pixel is impulse noise (that is, its value differs from the average
by more than 50), go to step 3;
if the central pixel is not impulse noise, go to step 4.
3. Modify the value of the central pixel in the window according to the median
filter algorithm.
4. Modify the value of the central pixel in the window according to the algo-
rithm of the FGG filter.
Table 4. Evaluation of image processing results by proposed approaches
MSE PSNR SSIM
Noised image 1613,751 16,052 0,446
Image processed by sequential use of filters 445,310 21,644 0,730
The image processed by the combined adaptive filter 425,972 21,837 0,741
Thus, it can be seen that the combination of adaptive median and FGG filters into
a single combined adaptive filter is appropriate and effective, because this filter is
better than the sequential applying of these filters according to all criteria [34, 35].
In order to be sure of the effectiveness of the combined method of filtering noise
in digital images, it was decided to conduct a comparative analysis of all considered
filters for three types of noise: impulse, Gaussian, and combined. The analysis was
carried out on 10 color images with different detail level, colors, contrast ant other
characteristics.
Table 5. The resultant comparison table of all methods
Number of points
Impulse noise Gaussian Combined
filtering noise filtering noise filtering
FCG filter 20 66 32
Combined use of median filter and FCG 56 36 69
Sequential use of median filter and FCG 55 31 58
Gaussian filter 30 44 21
Geometric averaging filter 10 26 10
Arithmetic averaging filter 40 64 51
Median filter 69 13 39
So it was proved the effectiveness of using the combination of median and FCG
filters to remove the combined noise. However, it should be emphasized that this
filtration method is worse for images that are distorted individually by additive
Gaussian noise or impulse noise.
5 Conclusions
In this paper, it was demonstrated that classical filtration methods, as well as those
that apply fuzzy logic approaches, cannot cope with the removal of the combined
noise type in images (a combination of impulse noise and an additive Gaussian).
These conclusions were made by calculating MSE, PSNR and SSIM criteria for pro-
cessed images.
Therefore, it was needed to develop an approach that would show an effective re-
sult for the removal of the combined noise type. A combination of a median filter and
a FCG filter was proposed for solving this problem. The results were verified by pro-
cessing 10 color images. It was experimentally proved the effectiveness of using the
proposed approach.
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