Edge Detection Technique Using ACO with PSO for Noisy Image

Abstract

In image processing, the edges of an image are those pixels whose intensity values changes drastically. Various techniques have been applied which rely on the ant colony optimization (ACO) for edge detection and the threshold value calculated for edge detection technique is either user defined or taken as the mean value of the obtained pheromone matrix. Other challenges to deal with edge detection are the presence of noisy environment, so to deal with it, in this paper, we define adaptive threshold value based on particle swarm optimization (PSO) for edge detection to overcome the limitation of existing ACO-based edge detection techniques. The experiment results have shown that the proposed technique has performed better under noisy environment over Sobel, Canny, and ACO-based technique both for objective criteria, i.e., restored edge images and subjective criteria, i.e., PSNR, precision, recall, and F-measure for test images in addition to reducing the execution time.