Image Edge Detection and Image Edge Enhancement: Numerical Experiment on High Pass Spatial Filtering

Edges represent discontinuities of image intensity in an image. High level digital image processing such as object recognition, segmentation and robot vision depend on the accuracy of edge detection. Adding product of the detected image edge and a scaling constant k to the original image is useful for image edge enhancement for better visual perception. Numerical Experiment on high pass spatial filtering for image edge detection and image edge enhancement is presented. Eleven alternative combinations of Mean filtering function hm, Gaussian filtering function hg and three versions of Laplacian filtering functions hL1, hL2 and hL3 are considered. Changes in Frequency Estimate, Brightness and Contrast produced by the eleven alternative combinations of filtering functions are measured and recorded. Results show that Laplacian functions are high pass filtering functions while Mean function and Gaussian function are low pass filtering functions. hL2 is found to be the best among the three versions of Laplacian filtering functions: the output of hL2 has highest Frequency Estimate and Brightness compared with hL1 and hL3. The combination hmhL2 (Laplacian of Mean) is the best choice for image edge detection as it produced image edge of moderate Frequency Estimate and Brightness which is free of false edges (noise). Low pass filtering functions are found to be more suitable for image edge enhancement than high pass filtering functions. The degree of image edge enhancement quality increases as scaling constant k increases from 0 to 0.8 beyond which diminishing returns set in. Keywords-edge; feature extraction; enhancement; spatial filtering; spatial frequency