Efficiency of Edge Detection Properties of Images Contaminated with Gaussian Noise of Different Noise Levels
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Abstract
In digital image processing, edge detection plays a crucial role. As edges within a digital image contribute to acquire critical insight of the object which are implicit in the image. As a view of fact, edges constitute boundaries between objects exists in the image. The extraction of these attributes can be utilized to greater extent in real time purpose. However it is arduous to extract out all the edges systematically without any consequences that may cause damage to the constitution of an image. This paper narrates the domination of various intensity of noise in the edge detection of images and comprehensive statistical metric to get insight of the capability of the proposed strategy. This strategy is utilized a modified canny method using s-membership function. In this technique, two images were considered and Gaussian noise is introduced to these images at various levels of intensity. In the next stage a Gaussian filter is exploited to denoise the image and edges are examined with the help of modified canny method using S-membership function. Various statistical parameters are evaluated to identify the performance of proposed strategy.
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