灰度熵和混沌粒子群的图像多阈值选取

Multi-threshold selection for an image based on gray entropy and chaotic particle swarm optimization

最大Shannon熵阈值选取方法仅仅依赖于图像灰度直方图的概率信息,而没有直接考虑类内灰度级的均匀性.为此提出了最大灰度熵的阈值选取方法.首先给出了灰度熵的定义及其单阈值选取方法,该灰度熵与现有的仅基于直方图分布的最大Shannon熵不同,直接反映了类内灰度级的均匀性;其次导出了量化图像直方图的灰度熵单阈值选取公式;最后将灰度熵单阈值选取推广到多阈值选取,提出了相应的快速递推算法,并进一步采用混沌小生境粒子群优化算法寻找最佳多阈值.实验结果表明,与最大Shannon熵单阈值选取和基于粒子群的最大Shannon熵多阈值选取方法相比,所提出方法的分割图像边缘、纹理更为准确,视觉效果明显改善.

The method of threshold selection based on maximal Shannon entropy depends only on the probability information from a gray image histogram and does not immediately consider the uniformity of the gray scale within the cluster.Considering these facts,a method of threshold selection based on maximal gray entropy was proposed.First,gray entropy was defined and the method of single threshold selection was given.Being different from maximal Shannon entropy based only on histogram distribution,the gray entropy reflects the uniformity of the gray scale immediately within the cluster.Then,the formulae of gray entropy based single threshold selection of a quantized image histogram were derived.Finally,the method of single threshold selection based on gray entropy was extended to multi-threshold selection.A corresponding fast recurring algorithm was proposed.Furthermore,a particle swarm optimization algorithm with a chaotic niche was adopted to find the best multi-threshold.Many experimental results show that,compared with the methods of single threshold selection based on maximal Shannon entropy and multi-threshold selection based on maximal Shannon entropy with particle swarm optimization,segmented images of the suggested method are more accurate in edge and texture,and their visual effect is improved significantly.