18-12-2012, 03:01 PM
NOISE REDUCTION BY FUZZY IMAGE FILTERING
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ABSTARCT
Fuzzy image processing is not a unique theory. It is
a collection of different fuzzy approaches to image
processing. Nevertheless, the following definition
can be regraded as an attempt to determine the
boundaries:
Fuzzy image processing is the collection of all
approaches that understand, represent and process
the images, their segments and features as fuzzy
sets. The representation and processing depend on
the selected fuzzy technique and on the problem to
be solved.
(From: Tizhoosh, Fuzzy Image Processing, Springer, 1997).
Fuzzy image processing has three main stages:
image fuzzification, modification of membership
values, and, if necessary, image defuzzification.
A new fuzzy filter is presented for the noise
reduction
of Gray Scale images corrupted with additive noise.
The filter consists of two stages. The first stage
computes a fuzzy derivative for eight different
directions. The second stage uses these fuzzy
derivatives to perform fuzzy smoothing by
weighting the contributions of neighboring pixel
values. Both stages are based on fuzzy rules which
make use of membership functions. The filter can
be applied iteratively to effectively reduce heavy
noise. In particular, the shape of the membership
functions is adapted according to the remaining
noise level after each iteration, making use of the
distribution of the homogeneity in the
image.Astatistical model for the noise distribution
can be incorporated to relate the homogeneity to
the adaptation scheme of the membership
functions. Experimental results are obtained to
show the feasibility of the proposed approach.
These results are also compared to other filters by
numerical measures and visual inspection.
INTRODUCTION
FUZZY LOGIC
The concept of Fuzzy Logic (FL) was conceived by
Lotfi Zadeh, a professor at the University of
California at Berkley, and presented not as a
control methodology, but as a way of processing
data by allowing partial set membership rather
than crisp set membership or non-membership. This
approach to set theory was not applied to control
systems until the 70's due to insufficient smallcomputer
capability prior to that time. Professor
Zadeh reasoned that people do not require precise,
numerical information input, and yet they are
capable of highly adaptive control. If feedback
controllers could be programmed to accept noisy,
imprecise input, they would be much more effective
and perhaps easier to implement.
WHAT IS FUZZY LOGIC?
In this context, FL is a problem-solving control
system methodology that lends itself to
implementation in systems ranging from simple,
small, embedded micro-controllers to large,
networked, multi-channel PC or workstation-based
data acquisition and control systems. It can be
implemented in hardware, software, or a
combination of both. FL provides a simple way to
arrive at a definite conclusion based upon vague,
ambiguous, imprecise, noisy, or missing input
information. FL's approach to control problems
mimics how a person would make decisions, only
much faster.
VARIOUS APPROACHES CONSIDERED IN SOLVING OUR PROBLEM
I considered a Gray Scale image for this Fuzzy
Filter System. And for that I used a Java,Java Applet
and Java Swings. For this Fuzzy filter , I was first
implemented fuzzy sets. Then I was introduced
fuzzy rules and using that I done image fuzzification
and implement fuzzy filter. And this filter is apply
on image pixels & done modification of membership
values where noise is present.