A sonar image segmentation algorithm based on quantum-inspired particle swarm optimization and fuzzy clustering

The underwater sonar image has the characteristics of complex background and heavy noise pollution. By using multi-bit quantum system to encode particles, a new sonar image segmentation algorithm based on quantum-inspired particle swarm and fuzzy clustering is proposed. Based on its own optimal particles and global optimal particles, the rotation angle is determined. By calculating the variance of the particle group fitness, the multi-bit quantum revolving gate is used to update the particle position in real time. The output of improved particle swarm optimization is used to initialize the K-mean clustering center to converge to the global optimal solution. Based on the idea of fuzzy membership matrix in FCM, combined with the isolated spatial information characteristics of the noise, the sonar image segmentation and the denoising are carried out. The experimental results show that the proposed algorithm can improve the global search ability of particle swarm optimization effectively. It is better than the quantum fuzzy clustering and quantum genetic algorithm in image segmentation. The analysis results of multiple real underwater sonar images show that the new optimization algorithm has faster convergence speed, better optimizing capacity and better segmentation results for sonar images.