Research on image edge detection algorithms based on fractional-order differentiation

The image edge is a region in the image that exhibits a clear discontinuity and change, which can serve as a reflection of the most fundamental attributes of the image. The research concentration in the field of image processing and computer vision is also on edge detection technology, which is employed to ascertain the contour details between various objects and regions of the image. This paper first examines the principles and methodologies of traditional edge detection algorithms, succinctly outlining the advantages and disadvantages of the Roberts, Prewitt, Sobel, and Canny operators. It then introduces representative algorithms for image edge detection based on fractional-order differentiation. Finally, it compares traditional edge detection algorithms with those based on fractional-order differentiation through experimental analysis, demonstrating that the latter exhibits superior performance in contour continuity and detail integrity. The application of fractional-order differential theory in image processing demonstrates significant potential.