基于改进探路者算法的多阈值图像分割

针对多阈值图像分割方法中存在的计算量大、运行时间长等问题，在标准探路者算法的基础上，引入Tent混沌映射初始化和自适应t分布策略，提出一种基于改进探路者算法的多阈值图像分割方法，该方法以Kapur熵为目标函数对最优分割阈值进行搜索。为了验证算法的有效性，首先通过标准测试函数验证改进探路者算法的收敛精度和收敛速度，然后将改进探路者算法与Kapur熵结合后应用于Berkeley图像数据集进行多阈值分割，并与标准探路者算法、飞蛾扑火算法、灰狼优化算法和粒子群算法进行比较和分析。实验结果表明，提出的改进探路者算法收敛速度更快、求解精度更高，较其他对比算法有着更好的分割效果，且PSNR与SSIM都有更好的表现，能有效解决多阈值图像分割问题。

Multilevel Thresholding Image Segmentation Using Improved Pathfinder Algorithm

There are some problems in multilevel threshold image segmentation, such as large amount of computation and long running time. A new multilevel threshold image segmentation method named improved pathfinder algorithm (IPFA) is proposed using Tent map and adaptive t-distribution strategy on the standard of pathfinder algorithm (PFA). This method uses Kapur’s entropy as the objective function to search the best segmentation threshold. In order to verify the effectiveness of the algorithm, the convergence accuracy and speed of IPFA are tested by benchmark functions at first. Then IPFA-Kapur is applied to multilevel threshold image segmentation and compared with standard PFA, moth-flame optimization (MFO), gray wolf optimization (GWO) and particle swarm optimization (PSO). Experimental results show that the proposed algorithm has faster convergence speed and higher segmentation accuracy, and has better segmentation effect than other comparison algorithms, and the peak signal to noise ratio (PSNR) and structural similarity (SSIM) have better performance, which can effectively solve the problem of multilevel threshold image segmentation.