Whale Optimization Algorithm Based Edge Detection for Noisy Image

Abstract

Edge in image processing is considered as those pixels whose intensity value changes drastically and finding the object boundary is the main task of any edge detection technique. There have been various Ant Colony Optimization (ACO), Particle Swarm Optimization (PSO) based techniques that have been applied to solve edge detection problem, but most of them have not considered the noisy environment which in itself makes edge detection further more difficult task and the user-defined threshold approach doesn't always give desired results. The paper proposes a Whale Optimization Algorithm (WOA) based edge detection technique with weighted fitness function including homogeneity, uniformity and average gradient magnitude as main factors for detecting the edges of additive gaussian noise images. The experiment results have shown that the proposed technique has performed better under noisy environment for conventional edge detectors: Sobel, Canny and ACO based technique for both objective criteria i.e. restored edge images and subjective criteria i.e. PSNR, Precision, Recall and F -measure.