基于改进差分进化算法的多阈值图像分割

阈值法是一种简单有效的图像分割技术.但是阈值法也有着明显的缺点，即阈值求解的计算量随阈值的增加而指数级增长.为克服多阈值图像分割计算量大、运算时间长的缺点，引入改进的差分进化算法，提出新的变异策略，采用自适应的缩放因子和交叉系数，并新增扰动策略.改进的算法将多阈值分割模型视为优化问题，将最大类间方差法作为目标函数，实现多阈值分割.实验结果表明，和其它算法相比，该算法不仅可以取得正确的分割结果，而且分割速度更快.

Multi-Threshold Image Segmentation Method Based on Improved Differential Evolution Algorithm

The threshold method is a simple and effective image segmentation technique.However,the threshold method also has obvious disadvantage,the amount of calculation for solving threshold appears to be exponential amplification with the increase of threshold.In order to overcome the shortcomings of large computation load and long computation time for multi-threshold image segmentation,we introduce an improved differential evolution algorithm,which proposes a new mutation strategy,adopts self-adaption scaling factor and cross factor,and newly adds Perturbation strategy.In order to achieve multi-threshold segmentation,the improved algorithm considers multi-threshold segmentation as an optimization problem whose objective function is formulated according to Otsu.Experimental results show that compared with other algorithms,the improved algorithm not only can achieve an accurate image segmentation result,but also has a faster speed.