Improving Canny edge detection algorithm using fractional-order derivatives

One of the purposes of edge detection is to use methods that be able to process visual information according to human needs. Therefore, an edge detector is reliable when evaluated by measurement criteria before use in computer vision tools. These criteria compute the difference between the ground truth edge map (reference image) and the original image. In this study, we propose an improved Canny edge detection method based on the fractional-order operators to extract the ideal edge map. Then, by changing the hysteresis thresholds, the thin edges are obtained by filtering gradient calculations based on fractional-order masks. In addition, we employ common fractional-order derivative operators to extract the edge strength and enhance image edge contrast. The plotted curves of the edge detection criteria show that the obtained edge map of the proposed edge detection operator, which is considered to be the minimal rating of  measurement, is visually and quantitatively closer to ground truth.