Multi-focus Image Fusion Algorithm Based on Improved Lion Swarm Optimization Algorithm

Addressing the limitations of the Lion Swarm Optimization (LSO) algorithm, such as its tendency to converge to local optima and its slow convergence rate, we propose an improved LSO algorithm that integrates Gaussian mapping and a somersault foraging strategy. Firstly, we advocate replacing the randomly generated initial population of the original algorithm with chaotic sequences generated via Gaussian mapping, thereby augmenting the diversity within the population. Secondly, the incorporation of the somersault foraging strategy is aimed at enhancing the diversity of optimization positions, thus bolstering the algorithm's resilience against local optima. Simulation experiments conducted on CEC2019 benchmark functions showcase notable enhancements in both convergence speed and solution accuracy with our proposed algorithm. Finally, the application of the improved LSO algorithm to multi-focus image fusion tasks reveals its superior performance in quantitative and visual assessments when compared against conventional techniques and genetic algorithm.