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.
Source: ACM
Authors: C. Mythili | V. Kavitha
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