Computational Intelligence - August 2012 - 26
operator called impulse detector, which decides
whether the pixel concerned needs filtering.
In this way, the filter restores only the pixels
that are identified by the detector as impulses
and leaves the other pixels (i.e., the uncorrupted pixels) unchanged. However, it is not
difficult to see that the performance of the
impulse detector guided filtering naturally
depends on the performance of the impulse
detector. Hence, use of methods from many
different areas of signal and image processing
that may provide information for classifying
image pixels has led to the development of many different
types of impulse detectors exploiting median filters [3], center-weighted median filters [4], boolean filters [5], edge detection kernels [6], homogeneity level information [7], statistical
tests [8], classifier based methods [9], rule-based methods [10],
level detection methods [11], pixel counting methods [12]
and soft computing methods [13]-[14].
Even though a significant majority of the impulse noise
removal operators are order statistics filters such as the median
based filters and their enhanced derivatives, various types of
mean filters have been employed for impulse noise removal
from digital images too [15]-[17]. These filters more or less
exhibit a similar performance as median based filters in terms
of both noise removal and detail preservation.
A final category of impulse noise removal operators contain
the filters based on nonlinear methods. These are usually more
complicated than the ones mentioned above but offer better
performance. The first group in this category include the operators based on computational intelligence techniques [18]-[23].
These operators are computationally more complex than the
others but yield very good noise suppression and detail preservation performance. The second and the last groups comprise
nonlinear filters [24]-[30] which combine the desired properties of the above mentioned filters. These filters again provide
much better filtering performance at the cost of increased
computational load.
In the last decade, type-2 fuzzy logic systems have been of
high research interest and applied to many different problems
in various areas of science and technology [31]-[44]. They have
also been successfully utilized in image processing for a number
of tasks including gray scale image thresholding [45], edge
detection [46]-[48], noise filtering [49]-[52], corner and edge
detection in color images [53], deinterlacing of video signals
[54], and image enhancement [55].
In a recent paper [51], we presented a noise filtering operator designed by employing type-2 fuzzy logic techniques and
shown that it offers excellent noise suppression performance
while effectively preserving small details and other useful information within the input image. In this paper, we propose
another novel application of type-2 fuzzy logic techniques, in
which a first order Sugeno type interval type-2 fuzzy inference
system is trained to function as an impulse detector. The proposed detector can be used to guide any impulse noise filter to
One of the most common problems encountered
during the acquisition and/or transmission of digital
images is the contamination of the image with
impulse noise. Due to this inevitable fact, the
acquired or received digital image must usually
be pre-processed by applying an appropriate
filtering operator to the image to suppress the
contaminating noise.
noise will dramatically decrease the performances of subsequent image processing tasks that are to be performed on
the image if it is not properly removed from the image.
Hence, the research efforts for the development of methods
and design of operators for the restoration and enhancement of digital images corrupted by impulse noise have
received high interest in the last few decades, which consequently led to the development of many different methods
and operators for identifying and canceling impulse noise
from digital images.
The most simple and natural approach to design an impulse
noise removal operator is to utilize the order statistics between
the corrupted pixel, to which the operator is to be applied to
obtain its restored value, and other pixels in its neighborhood.
The simplest impulse noise removal operator, the standard median filter [1], exploits the rank order information between the
pixels in the filtering window and attempts to restore the pixel
at the center of the window by replacing it with the median
pixel of the window. This approach is computationally very
efficient and provides an acceptable noise removal performance
for low noise densities. However, both noise suppression and
detail preservation performances radically decrease as the noise
density increases. Moreover, the standard median filter considerably blurs the output image and distorts the texture and small
details in the image. Various derivatives of the standard median
filter modifying its simple filtering behavior have been proposed to avoid this problem. In order to improve the standard
median filtering, some operators were proposed as presented in
[2] where these operators usually operate by performing a custom weighting on certain pixels in the filtering window, which
results in some improvement in detail preservation performance but at the cost of some decrease in the noise suppression performance.
All impulse noise removal operators more or less distort
the small details in the image during noise filtering, which
causes some blurring in the output image. One major cause
of this undesirable effect is the simple and efficient (but inappropriate) filtering behavior of applying the same filtering
operation to all pixels of the input image. This negative effect
can be significantly reduced by first observing whether the
pixel under filtering is noisy or not and then filtering it only
if it is noisy. This selective filtering behavior can be achieved
by guiding the noise filter by an additional image processing
26
IEEE COMPUTATIONAL INTELLIGENCE MAGAZINE | AUGUST 2012
Table of Contents for the Digital Edition of Computational Intelligence - August 2012
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