Abstract
Image enhancement is a crucial pre-processing step to be performed for various applications where object recognition, identification, verification is required. Among various image enhancement methods, edge enhancement has taken its importance as it is widely used for understanding features in an image. Several types of edge detectors are available for certain types of edges. If edges are enhanced and clear, the reliability for feature extraction increases. The Quality of edge detection can be measured from several criteria objectively. In this paper, a novel algorithm for edge enhancement has been proposed for multiple types of images. The features can be extracted clearly by using this method. For comparison purpose Roberts, Sobel, Prewitt, Canny, and Log edge operators are used and their results are displayed. Experimental results demonstrate the effectiveness of the proposed approach.
Highlights
Edge detection is a critical element in image processing, since edges contain a major function of image information
The paper [5] presents a new method for edge enhancement in synthetic aperture radar (SAR) images based on the exploitation of the information provided by the wavelet coefficients
Some of the images taken for experiment are shown in the figure 4
Summary
Edge detection is a critical element in image processing, since edges contain a major function of image information. Many edge detection algorithms have been developed based on computation of the intensity gradient vector, which, in general, is sensitive to noise in the image [1]. This paper presents a robust and unsupervised edge enhancement algorithm based on a combination of wavelet coefficients at different scales. The paper [5] presents a new method for edge enhancement in SAR images based on the exploitation of the information provided by the wavelet coefficients. Many edge detection algorithms have been developed based on computation of the intensity gradient vector as well as fuzzy enhancement. We propose a novel algorithm to enhance the contrast of an image by pre-designed SCIENCE [8] before enhancing the edges of the image.
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