Abstract
In computer vision, edge detection is a crucial step in identifying the objects’ boundaries in an image. The existing edge detection methods function in either spatial domain or frequency domain, fail to outline the high continuity boundaries of the objects. In this work, we modified four-directional mini Kirsch edge detection kernels which enable full directional edge detection. We also introduced the novel involvement of the proposed method in image sharpening by adding the resulting edge map onto the original input image to enhance the edge details in the image. From the edge detection performance tests, our proposed method acquired the highest true edge pixels and true non-edge pixels detection, yielding the highest accuracy among all the comparing methods. Moreover, the sharpening effect offered by our proposed framework could achieve a more favorable visual appearance with a competitive score of peak signal-to-noise ratio and structural similarity index value compared to the most widely used unsharp masking and Laplacian of Gaussian sharpening methods. The edges of the sharpened image are further enhanced could potentially contribute to better boundary tracking and higher segmentation accuracy.
Highlights
Edges in a digital image are the pixels where their gray level intensity changes abruptly compared to their local neighbouring pixels
A high accuracy edge detection rate indicates the successful classification of true edge pixels or true positives and true non-edge pixels or true negatives
We proposed a four-directional mini Kirsch edge detection method that inherited the characteristics of the eight-directional Kirsch compass edge detector and is sufficient to detect edges from different directions
Summary
Edges in a digital image are the pixels where their gray level intensity changes abruptly compared to their local neighbouring pixels. 229 computation as gradient operators, thresholding to keep strong edges while removing the weak one and non-maximum suppression for edge thinning process [14] Sharpening is another crucial stage to boost the edge detection performance. It helps to emphasize the tiny details and boundaries of objects in an image [15]. The approximation could be affected by the level of noise and the magnitude of edges [16] Overall, these operators cannot result in accurate edge detection especially those thin and faint edges. The edge detection performance of the proposed method was compared with Sobel, Prewitt, Roberts, LoG, Canny, and Kirsch algorithms while its sharpening effect was examined together with unsharp masking and LoG
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