Edge Detection Optimization Using Fractional Order Calculus

In computer vision and image processing, time and quality are major factors taken into account. In edge detection process, the smoothing operation by a low-pass filter is commonly performed first in order to reduce noise effect. However,performing the smoothing operation partially requires additional computational time and alters true edges as well. Attempting to resolve such problems, a new approach dealing with edge detection optimization is addressed in this paper. For this purpose, a short edge detector algorithm without smoothing operation is proposed and investigated. This algorithm is based on a fractional order mask used as kernel of convolution for edge enhancement. It has been shown that in the proposed algorithm, the smoothing pre-process is no longer necessary; because, the efficiency of our fractional order mask is expressed in term of immunity to noise and the capability of detecting edges. Simulation results show how the quality of edge detection can be enhanced on adjusting the fractional order parameter. Then, our proposed edge detection method can be very useful in real time applications in some fields such as, satellite and medical imaging