Image Enhancement ANPSO Processing Technology Based on Improved Particle Swarm Optimization Algorithm

• Published in: IAENG international journal of computer science Vol. 51; no. 11; p. 1781
• Main Authors: You, Zhangping, Yi, Dajian, Fang, Zheng, Zhang, Wenhui
• Format: Journal Article
• Language: English
• Published: Hong Kong International Association of Engineers 01.11.2024
• Subjects: Adaptive algorithms; Ant colony optimization; Chaos theory; Genetic algorithms; Image enhancement; Image quality; Particle swarm optimization
• ISSN: 1819-656X; 1819-9224

To improve the efficiency and effectiveness of image enhancement, a novel Ant Colony Natural Inspired Particle Swarm Optimization (ANPSO) algorithm is proposed. This algorithm integrates Ant Colony Optimization (ACO) and Particle Swarm Optimization (PSO) using natural inspiration and chaos theory to enhance image quality. By employing a nonlinear random incremental method, it designs adaptive inertia weights to improve global search capabilities and stability. Furthermore, based on the pheromone release and path optimization mechanisms of the ant colony algorithm, it enhances the information transmission mechanism in PSO, allowing for more efficient information sharing among particles and strengthening cooperative search abilities. Experimental comparisons with Genetic Algorithm (GA), ACO, and PSO demonstrate that ANPSO improves Peak Signal-toNoise Ratio (PSNR), Structural Similarity Index (SSIM), and algorithm convergence by 8.3%, 7.6%, and 9.7%, respectively. These results highlight the significant performance advantages of ANPSO in image enhancement tasks.