基于金字塔演化策略的彩色图像多阈值分割

Multi-threshold Segmentation for Color Image Based on Pyramid Evolution Strategy

传统智能优化算法对彩色图像进行分割未能考虑种群间的竞争与协作关系,从而造成局部最优问题,影响了分割效果。为了提高分割效果,提出了一种改进金字塔演化策略(Improved Pyramid Evolution Strategy,IPES)。首先设计了一种适合彩色图像多阈值分割问题的自适应搜索算子,扩大各层级搜索空间,提高了算法的寻优能力;然后利用种群间的竞争与协作关系以解决局部最优问题,进而提高求解精度和分割效果;最后以最大类间方差(Otsu)为优化目标,利用现有的标准测试图像对算法性能进行测试,并将IPES算法与其他8种算法的性能进行比较。实验结果表明,经IPES算法分割后的图像峰值信噪比值在28~35 dB之间,比改进树种算法和传统的粒子群算法以及差分进化算法至少提高了10 dB;结构相似性值在89%~97%之间,至少提高了3个百分点,分割后的图片质量更好,结构相似性更高。因此,该算法在求解彩色图像多阈值分割问题时具有良好的性能。

In view of the fact that traditional intelligent optimization algorithms for multi-threshold segmentation of color images fall to consider the competition and cooperation between populations,which results in local optimization problems that affect the segmentation effect.In order to improve the segmentation effect,an improved pyramid evolution strategy(IPES)is proposed.The algorithm designs an adaptive search operator suitable for the multi-threshold segmentation problem of color images;expands the search space at all levels,improves the optimization ability of the algorithm;takes Otsu as the optimization goal and uses the competition and cooperation relationship between populations to solve the local optimization problem,thereby improving the accuracy of the solution and the effect of segmentation.The performance of IPES is tested on existing standard test images and compared with other eight algorithms.Experimental results show that the peak signal-to-noise ratio of the image segmented by IPES algorithm is between 28~35 dB,which is at least 10 dB higher than that of the improved tree-seed algorithm and traditional particle swarm algorithm and differential evolution algorithm;the structural similarity is between 89%~97%,increased by at least 3%.The image quality after segmentation is better and the structural similarity is higher.Therefore,the algorithm has good perfor-mance in solving multi-threshold segmentation problem of color images.