A multifractal edge detector

Abstract

Significant research has been conducted into image edge detection, usually focusing on gradient and higher order derivative approaches. Recent development in the field suggests the incorporation of scale factor in filter design; this is to increase robustness against noise and to facilitate the process of generating multilevel edge maps. While multifractal analysis technique uses scale factors, few researchers examine the effectiveness of this technique in image edge map generation due to its poor efficiency. Herein, we propose F-MED, a fast multifractal edge detector for grayscale images based on generating an edge map from different low frequency versions of the original image by means of frequency domain Gaussian low pass filters. The quality of results is evaluated subjectively via visual assessment and quantitatively using a set of full-reference and non-reference measures. Results show that the proposed F-MED can be leveraged to support not only high efficiency, but also quality improvement and high robustness against blurring effect and Rayleigh noise.