基于改进蜉蝣算法优化多阈值图像分割

Optimized Multi-threshold Image Segmentation Based on Improved Mayfly Algorithm

针对图像多阈值分割中存在分割效率低、计算时间长以及精度不高等问题,提出了一种基于改进蜉蝣算法的多阈值图像分割算法。首先,在初始化阶段引入类随机采样方法中的Sobol序列,增强种群的遍历性和多样性;其次,提出一种自适应非线性惯性权重,平衡了全局与局部寻优能力,提高了算法的收敛效率,利于种群向最优解逼近;最后,采用指数熵作为计算适应度的目标函数,通过改进蜉蝣算法对图像分割的多阈值组合进行寻优,确定最优分割阈值。为了验证该改进算法的有效性,选择了伯克利图像来进行分割验证,并与其他智能算法进行比较。实验结果表明:该改进算法在分割准确性、计算时间、结构衡量指标(structure similarity index measure,SSIM)和峰值信噪比(peak signal-to-noise ratio,PSNR)上均优于对比算法,能快速有效地解决复杂多目标图像的多阈值分割问题,具有较强的工程实用性。

To address the problems of low segmentation efficiency,long computation time and low accuracy in multi-threshold image segmentation,a multi-threshold image segmentation algorithm based on an improved mayfly algorithm was proposed.Firstly,the Sobol sequence in the initialization stage was introduced to enhance the traversal and diversity of the population.Secondly,an adaptive nonlinear inertia weight was proposed to balance the global and local merit-seeking ability,improved the convergence efficiency of the algorithm,and facilitated the approximation of the population to the optimal solution.Finally,the exponential entropy was used as the objective function to calculate the fitness,and the multi-threshold combination of image segmentation was determined by the improved mayfly algorithm.Finally,the multi-threshold combination of image segmentation was optimized by improving the mayfly algorithm to determine the optimal segmentation threshold.In order to verify the effectiveness of this improved algorithm,Berkeley images were selected for segmentation verification and compared with other intelligent algorithms.The experimental results show that the improved algorithm outperforms the comparison algorithm in segmentation accuracy,computation time,structure similarity index measure(SSIM)and peak signal-to-noise ratio,(PSNR)and can quickly and effectively solve the multi-threshold segmentation problem of complex multi-target images,which has strong engineering practicality.