计算机应用与软件2025，Vol.42Issue(5)：224-230,254,8.DOI:10.3969/j.issn.1000-386x.2025.05.031
基于改进狮群算法的混合图像盲分离
HYBRID IMAGE BLIND SEPARATION BASED ON IMPROVED LION SWARM OPTIMIZATION
摘要
Abstract
In view of the low separation performance of traditional independent component analysis methods for blind source separation,a blind source separation method based on improved lion swarm optimization is proposed and applied to image blind separation.On the basis of the original lion swarm optimization,the optimization combined with the strong local search ability of butterfly optimization algorithm and the excellent evolutionary mechanism of immune concentration selection,and adjusted the search balance of the algorithm through the inertia weight based on vector distance.The algorithm took the negative entropy and kurtosis of the signal as the objective function,and realized the blind separation of mixed signals by solving the objective function.Simulation results show that the proposed algorithm can effectively separate noisy mixed images,has better separation performance than the contrast algorithm,and has better separation performance under the kurtosis based objective function.关键词
盲源分离/独立分量分析/狮群算法/蝴蝶算法/免疫浓度选择/惯性权重Key words
Blind source separation/Independent component analysis/Lion swarm optimization/Butterfly optimization algorithm/Immune concentration selection/Inertia weight分类
信息技术与安全科学引用本文复制引用
夏清雨,丁元明,张然,杨阳..基于改进狮群算法的混合图像盲分离[J].计算机应用与软件,2025,42(5):224-230,254,8.基金项目
国家自然科学基金项目(61901079) （61901079）
装备发展部领域基金一般项目(61403110308). （61403110308）
