A novel construction of a fractional derivative mask for image edge analysis

In this paper, we present a new fractional mask based on the Prabhakar derivative for edge detection. In comparison with other fractional masks, more parameters are generated, hence, we have more degree of freedom and so, in general, we expect to see better results. We choose some selected well known synthetic images with realistic geometry in our experiments. The comparisons are computed based on the standard peak signal to noise ratio (PSNR) and mean square error (MSE).