基于改进灰狼优化算法的多阈值图像分割研究

针对传统阈值分割方法在确定最优阈值时容易陷入局部最优、效率不足和对噪声的高敏感性等问题, 文章提出一种结合多种策略的灰狼优化(modified strategy integrated grey wolf optimizer, MSI-GWO)算法, 并将其用于基于最小对称交叉熵的阈值图像分割。该算法引入改进的 Tent 混沌进行初始化, 以增强全局搜索能力并加速优化进程; 通过改进控制参数, 辅助种群跳脱局部极值; 同时加入随机游走策略, 有效提升对最优解的搜索效率。经过 6 个标准测试函数的验证, MSI-GWO 算法在收敛性能上相较于传统智能优化算法表现更佳。在应用于基于最小对称交叉熵的阈值图像分割时, MSI-GWO 算法在特征相似性指数、结构相似性指数和峰值信噪比等性能指标上, 随着阈值数的增加表现出明显的性能提升, 验证了该算法在图像分割领域的应用潜力。

Addressing the issues commonly encountered in traditional threshold segmentation methods, such as susceptibility to local optima, inefficiency, and high sensitivity to noise, this paper introduces a modified strategy integrated grey wolf optimizer (MSI-GWO), which is applied to threshold image segmentation based on the minimum symmetric cross-entropy. The algorithm incorporates an improved Tent chaos initialization to enhance global search capability and accelerate the optimization process. It modifies control parameters to help the population escape local extrema. Furthermore, the integration of a random walk strategy significantly improves the efficiency of searching for the optimum solution. Verified by six standard test functions, MSI-GWO shows superior convergence performance compared to traditional intelligent optimization algorithms. When applied to threshold image segmentation based on minimum symmetric cross-entropy, MSI-GWO demonstrates a marked improvement in performance metrics, such as feature similarity (FSIM), structural similarity (SSIM), and peak signal-to-noise ratio (PSNR), with an increase in the number of thresholds, confirming the application potential of the algorithm in the field of image segmentation.