论文丨Xin-Yu LIU, and Yi-Fei PU*： Image Edge Detection Based on Fractional-Order Ant Colony Algorithm

本文（Image Edge Detection Based on Fractional-Order Ant Colony Algorithm）原载Fractal and Fractional，由四川大学蒲亦非教授等科研人员创作，系四川大学智慧法治超前部署学科系列学术成果。后续会持续分享四川大学智慧法治超前部署学科系列学术成果，欢迎大家阅读。

Abstract: Edge detection is a highly researched topic in the field of image processing, with numerous methods proposed by previous scholars. Among these, ant colony algorithms have emerged as a promising approach for detecting image edges. These algorithms have demonstrated high efficacy in accurately identifying edges within images. For this paper, due to the long-term memory, nonlocality, and weak singularity of fractional calculus, fractional-order ant colony algorithm combined with fractional differential mask and coefficient of variation (FACAFCV) for image edge detection is proposed. If we set the order of the fractional-order ant colony algorithm and fractional differential mask to 𝑣=0, the edge detection method we propose becomes an integer-order edge detection method. We conduct experiments on images that are corrupted by multiplicative noise, as well as on an edge detection dataset. Our experimental results demonstrate that our method is able to detect image edges, while also mitigating the impact of multiplicative noise. These results indicate that our method has the potential to be a valuable tool for edge detection in practical applications.

Xin-Yu LIU, and Yi-Fei PU*. Image Edge Detection Based on Fractional-Order Ant Colony Algorithm, Fractal and Fractional, vol. 7, no. 6, Article ID 420, 2023.（论文下载）