基于差分进化的二维熵图像分割

Image Segmentation Based on Differential Evolution and 2-D Entropy

二维熵图像分割方法利用图像的局部空间信息,分割结果优于一维熵分割法,但计算效率较低.遗传算法和粒子群算法等最优化算法能提高二维熵图像分割方法的效率,却不能保证获得全局最优阈值.为此,提出一种基于差分进化算法的二维熵图像阈值分割法,用局部搜索策略提高搜索最优阈值的精度.实验表明,所提出的方法有良好的鲁棒性,能保证获得最优阈值.与基本的二维熵图像分割法相比,分割速度有很大的提高.

Image segmentation based on 2-D entropy uses local space information of images and has better segmentation results than 1-D entropy based methods, but the computation efficiency is low. Commonly used optimization algorithms such as genetic algorithm and particle swarm optimization can improve efficiency of 2-D entropy thresholding, but cannot ensure the optimal threshold values. In the present paper, an image segmentation approach based on differential evolution and 2-D entropy is proposed to avoid drawbacks of the above methods. A local search strategy is used to further improve precision of the optimal threshold. Experimental results indicate that the proposed method is robust, and can acquire the optimal threshold values with much faster running speed than the primary 2-D entropy thresholding method.