Images
are often degraded by noises. Noise can occur during image capture,
transmission, etc. Noise removal is an important task in image processing. In
general the results of the noise removal have a strong influence on the quality
of the image processing technique. Several techniques for noise removal are
well established in color image processing. The nature of the noise removal
problem depends on the type of the noise corrupting the image. In the field of
image noise reduction several linear and non linear filtering methods have been
proposed. Linear filters are not able to effectively eliminate impulse noise as
they have a tendency to blur the edges of an image.
On
the other hand non linear filters are suited for dealing with impulse noise.
Several non linear filters based on Classical and fuzzy techniques have emerged
in the past few years. For example most classical filters that remove
simultaneously blur the edges, while fuzzy filters have the ability to combine
edge preservation and smoothing. Compared to other non linear techniques, fuzzy
filters are able to represent knowledge in a comprehensible way. In this paper we
present results for different filtering techniques and we compare the results
for these techniques.
SOFTWARE REQUIREMENTS
HARDWARE
REQUIREMENTS
·
Processor : Pentium Series
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Hard disk : 40 GB
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RAM : 2GB
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Keyboard : 110 Keys enhanced
· Mouse : Logitech
SOFTWARE
REQUIREMENTS
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Operating System : Windows XP/7
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Front End : .NET 2008
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Coding Language : C#
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