关键词: 图像分割, 人工鱼群算法, 小波变换, 噪声抑制

Abstract: To solve the problem of large calculation and sensitivity to noise disturbance in image segmentation, this paper combines a novel intelligence optimization algorithm, i.e. the artificial fish swarm algorithm, with  discrete wavelet transform, and proposes a parallel segmentation method for noise-polluted images. The suggested method employs a special scheme to decide the step of individuals in the fish swarm to improve the speed of convergence, which integrates the fixed step and the adaptive step. On the other hand, discrete wavelet transform is introduced to improve the signal-to-noise ratio of segmented images, in which appropriate wavelet coefficients are selected to reconstruct a noise-suppressed image. The 2D Otsu method serves as the fitness function for the improved AFSA to obtain an optimal threshold. Experimental results show that the proposed method is superior to the method proposed by Pan and Wu in terms of segmenting effect and noise reduction.

Key words: image segmentation, Artificial Fish Swarm algorithm（AFSA）, wavelet transform, noise reduction