Modified Bird swarm algorithm for edge detection in noisy images using fuzzy reasoning

Abstract

ABSTRACTA new bio-inspired edge detection approach is proposed to deal with the noisy images using a combination of bird swarm algorithm (BSA) and fuzzy reasoning. BSA is based on the behaviour of the birds. The birds fly through each pixel while they forage for the food and detect the edge pixels and noisy pixels that fall in their path. The direction in which the birds fly is found using fuzzy rule-based system. The pixels are classified as edge and non-edge pixels using the concept of thresholding. The noisy pixels are removed by the birds using fuzzy impulse noise detection and reduction method. The technique has been evaluated on two standard image datasets for quantitative and qualitative analysis. The results clearly indicate significant improvement over the other bio-inspired approaches, namely, Genetic Algorithm (GA), Particle Swarm Optimisation (PSO), Ant Colony Optimisation (ACO), Bacterial Foraging Algorithm (BFO), Deep Learning approach, fuzzy with BFO for noisy images, Neuro-fuzzy approach and PSO particularly for images having impulse noise in terms of Entropy, Kappa Value, Pratt’s Figure of Merit (FoM) and Structural Similarity Index Measure (SSIM). The proposed approach works well to detect the continuous, thin and smooth edges in the presence of 5–40% noise density.