基于改进麻雀算法的最大2维熵分割方法

为了提高最大2维熵分割的性能,提出了基于改进麻雀算法的最大2维熵分割方法,可减小运算量并且缩短计算时间.首先,融合反向学习策略和自适应t分布变异,引入精英粒子,以扩大算法搜索范围,增加算法后期局部搜索能力;其次,使用萤火虫机制,对最优解进行扰动变异,进一步增加种群多样性;最后,采用提出的改进麻雀算法寻找图像最大2维熵,...

Maximum 2-D entropy image segmentation method based on improved sparrow algorithm

In order to improve the performance of the maximum 2-D segmentation,an image segmentation method based on improved sparrow algorithm(ITSSA)was proposed,which can decrease the amount of computation and shorten the time.Firstly,the reverse learning strategy and adaptive t-distribution variation were combined,while elite particles were introduced to expand the search range of the algorithm and to increase the local search ability of the algorithm in the later stage.Secondly,the firefly mechanism was used to perturb and mutate the optimal solution for the further increasement of the population diversity.Finally,the improved sparrow algorithm was used to find the maximum 2-D entropy of the image,and then the optimal threshold segmentation image was obtained.The results show that,the average running time of the proposed algorithm in the four images is 0.3695s,which is much lower than 1.7547s of the basic two-dimensional entropy algorithm and 5.7936s of the basic two-dimensional Otsu algorithm.The global search and local optimization ability of ITSSA,compared with the original sparrow algorithm,improves a lot,and the proposed segmentation method in this paper greatly shortens the traditional maximum 2-D entropy image segmentation method of running time.Apart from that,both the peak signal to noise ratio and the feature similarity index of this method increase,which has a certain application value.