Abstract
In this paper, a novel method of hybrid filter for denoising digital images corrupted
by mixed noise has been presented. The proposed design of hybrid filter utilizes the
concept of neuro fuzzy network and spatial domain filtering. This method incorporates
improved adaptive wiener filter and adaptive median filter to reduce white Gaussian noise
and impulse noise respectively. Selection of filters depends upon the performance of the
impulse noise detection process. The edge detector is capable of extracting edges from
filtered images which has been blurred due to different filtering actions. Optimization of
neuro fuzzy network training with its internal parameters is collectively accomplished with
different natural and synthetic images. Data accomplished from the edge detector, noise
filter with the corrupted image together form the training data set. The most distinctive
feature of the proposed operator over most other operators is that it offers excellent line,
edge, detail, and texture preservation performance while, at the same time, effectively
removing noise from the input image.
Keywords: Image restoration, neuro fuzzy spatial filter, Hybrid filter, noise reduction
1. Introduction
Any image acquired by a device is susceptible of being degraded by the environment of acquisition and
transmission. The restoration of images tries to minimize the effects of these degradations by means of
a filter. Therefore, a fundamental problem in the image processing is the improvement of their quality
through the reduction of the noise that they can contain being often known as "cleaning of images". A
great variety of techniques dedicated to carry out this task exist. Each of them depends on the types of
the noise in images. During image acquisition, the photoelectric sensor induces the White Gaussian
noise due to the thermal motion of the electron. Many filters can be used to remove this type of noise;
the most famous one is Wiener filter. On the other hand, with the unstable transferring of network
some image data may be lost and impulse noise is combined into the image. To remove the impulse
noise, many filters are designed; a simple and effective one is Median filter. Gaussian and Impulse
noise together named as mixed noise. Neither Wiener filter nor Median filter alone can efficiently
reduce this mixed noise. This in turn insists the need for the investigation of new filters.
Design of Hybrid Filter for Denoising Images Using Fuzzy Network and Edge Detecting 6
In recent years many researchers are interested in this area and study the performance of the
noise removing filters for image transmissions. Several filters have been studied and implemented for
noise reduction. Median operation is combined into sigma filter to enhance the polluted image by
L.Alparone et al [1]. An enhanced version of Lee’s sigma filter is derived for filtering of images
affected by multiplicative noise with speckle statistics. A new edge-preserving filter which is called the
mean and median hybrid (MMH) filter is developed to achieve all kinds of noise removal, as well as
edge preservation [2]. Hybrid filter that consists of a nonlinear filter and a fuzzy weighted linear filter
is derived to reduce the mixed noise. They adopted the first part uses the statistics techniques are used
to remove the large magnitude impulsive noise then the second part uses a weighted average linear
filter to remove additive Gaussian noise and small ripple impulsive noise [3]. Three variants are
combined in trimmed mean filter by fuzzy set to get better noise smoothing result [4]. J.H.Wang et al
proposed histogram method is used as the input of fuzzy filter to remove the heavy tailed noise [5].
Chio and Krishnapuram [6] developed one new approach to image enhancement based on fuzzy
logic technique. Here, three filters have been introduced for removing impulse noise, smoothing out
non-impulse noise and enhancing edges. Histograms of homogenous image regions are used to
characterize and classify the corrupting noise [7]. The histogram information of the input image is used
to determine the parameters of the membership functions of an adaptive fuzzy filter. The filter is then
used for the restoration of noisy images.
The novel hybrid filter combines the advantages of the improved adaptive wiener filter and
bilinear interpolation filter for reducing both the white Gaussian noise and impulse noise [8]. Stefan
Schulte et al [9] proposed a new filter called fuzzy impulse noise detection and reduction method
(FIDRM). A new class of nonlinear filters called vector median-rational hybrid filters (VMRHF) for
multispectral image processing is devised by Lazhar Khriji and Monecef Gabbouj [10]. This filter is a
vector rational operation over three sub filters, these filters combine the behavior of rational functions
and vector median filters.
A novel switching median filter incorporating with an impulse noise detection method called
the boundary discriminative noise detection (BDND) is developed for effectively denoising extremely
corrupted images [11]. To determine whether the current pixel is corrupted, the BDND algorithm first
classifies the pixels of a localized window, centering on the current pixel, into three groups namely
lower intensity impulse noise, uncorrupted pixels, and higher intensity impulse noise. A new operator
for restoring digital images corrupted by impulse noise is presented. This operator is a filter obtained
by combining a median filter, an edge detector, and a neuro-fuzzy network. The internal parameters of
the neuro-fuzzy network are adaptively optimized by training [12].
A majority of above-mentioned filtering methods more or less has the drawback of removing
thin lines, distorting edges and blurring fine details in the image during noise removal process. In the
last few years, there has been a growing interest in the applications of soft computing techniques, such
as neural networks and fuzzy systems, to the problems in digital image processing [4], [9], and [12].
The proposed operator is a hybrid filter constructed by appropriately combining the noise filter,
and edge detector with neuro fuzzy network. The rest of the paper is organized as follows. Section 2
explains the structure of the hybrid filter and its building blocks and the implementation of the current
work to the test images are discussed. Results of the experiments conducted to evaluate the
performance of the suggested algorithm and comparative discussion of these results are projected with
tables in Section3.

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