Abstract
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.
Subject
Electrical and Electronic Engineering,Computer Networks and Communications,Hardware and Architecture,Signal Processing,Control and Systems Engineering
Reference34 articles.
1. Quantum Image Edge Extraction Based on Improved Sobel Operator;Zhou;Int. J. Theor. Phys.,2019
2. Liu, Y., and Long, Y. Image Edge Extraction Based on Fuzzy Theory and Sobel Operator. Proceedings of the 2016 IEEE 20th International Conference on Computer Supported Cooperative Work in Design (CSCWD) IEEE.
3. Edge and Line Oriented Contour Detection: State of the Art;Papari;Image Vis. Comput.,2011
4. Roberts, , and Lawrence, G. Machine Perception of Three-Dimensional Solids, 1963.
5. Sobel, I. Camera Models and Machine Perception, 1970.