An Image Edge Detection Method Based on Fractional-Order Grey System Model

The detection of edges in images is a pressing issue in the field of image processing. This technique has found widespread application in image pattern recognition, machine vision, and a variety of other areas. The feasibility and effectiveness of grey theory in image engineering applications have prompted researchers to continuously explore it. The grey model (GM (1,1)) with the first-order differentiation of one variable is the grey prediction model that is most frequently used. It is a typical trend analysis model and can be used for image edge detection. The traditional integer-order differential image edge detection operator has problems such as blurred and discontinuous edges, incomplete image details, and high influence by noise. We present a novel grey model for detecting image edges based on a fractional-order discrete operator in this paper. To improve the features of the original image, our model first preprocesses it before calculating the prediction of the original image using our fractional-order cumulative greyscale model. We obtain the edge information of the image by first subtracting a preprocessed image from the predicted image and then eliminating isolated noise points using the median filtering method. Based on the discrete wavelet transform, image edges are finally extracted. The comparison experiments with a traditional edge detection operator show that our algorithm can accurately locate the image edges, the image edges are clear and complete, and this model has better anti-noise performance.

Automated summary

The detection of edges in images is an important issue in image processing. This paper proposes a novel grey model based on a fractional-order discrete operator for detecting image edges. The model preprocesses the image, calculates the prediction, subtracts the preprocessed image from the predicted image, eliminates noise points, and finally extracts the image edges using discrete wavelet transform. The experimental results show that the proposed model accurately locates the image edges and has better anti-noise performance compared to traditional edge detection operators.