Image Edge Detection Based on Fractional-Order Ant Colony Algorithm

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 promising approach for detecting edges. These demonstrated high efficacy accurately identifying edges within images. For this paper, due to long-term memory, nonlocality, and weak singularity fractional calculus, fractional-order algorithm combined differential mask coefficient variation (FACAFCV) edge proposed. If we set order v=0, method propose becomes an integer-order method. We conduct experiments on images that are corrupted multiplicative noise, well dataset. Our experimental results demonstrate our able detect edges, while also mitigating impact noise. indicate has potential be valuable tool practical applications.