Image Edge Enhancement In Spatial and Frequency Domains

Abstract

Filtering helps to enhance the image by removing noise. Spatial filtering for image edge detection and image edge enhancement and frequency filtering for image edge enhancement is presented. In spatial domain, eleven alternative combinations of Mean filtering function h_m, Gaussian filtering function h_g and three versions of Laplacian filtering functions h_L1, h_L2 and h_L3 are considered. Changes in Frequency Estimate, Brightness and Contrast produced by the eleven alternative combinations of filtering functions are measured. Laplacian functions are high pass filtering functions while Mean function and Gaussian function are low pass filtering functions. 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. In frequency domain, both low pass and high pass filtering methods are performed. A low-pass filter is a filter that passes low-frequency signals and attenuates signals with frequencies higher than the cut-off frequency. There are several standard forms of low pass filters they are Ideal, Butterworth and Gaussian low pass filter. A high-pass filter is a filter that passes high frequencies well, but attenuates frequencies lower than the cut-off frequency. Sharpening is fundamentally a high pass operation in the frequency domain. There are several standard forms of high pass filters such as Ideal, Butterworth and Gaussian high pass filter.