10-06-2014, 03:39 PM
EXTIRPATING NOISE FROM IMAGES AND SECURING MULTICAST TRAVERSAL
EXTIRPATING NOISE FROM IMAGES.doc (Size: 483.5 KB / Downloads: 17)
ABTRACT
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. 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. Using Decision Based Unsymmetrical Trimmed Median Filter(DBUTM),Network layer security based on Kerberos protocol images are securely transmitted in a multicast environment Empirical analysis of performance of protocols(ipv4,ipv6) is done by the traversal of images with varying noise.
1.INTRODUCTION
The images corrupted by impulse noise[2] are often occurred in practice. Among all the methods for removal of impulse noise, the median filter[7] is used widely because of its effective noise suppression capability[3] and high computational efficiency.
In this work an improved algorithm Decision Based Unsymmetrical Trimmed Median Filter(DBUTM)[1] proposed for impulse noise removal, which is able to restore images, corrupted by very intensive salt-and-pepper impulse noise, efficiently. The new algorithm has large performance in removal of impulse noise from digital images while having a relatively low complexity. This filter is good at detecting noise even at a high noise level.