变精度最小平方粗糙熵的图像分割算法

An image segmentation algorithm using variable precision least square rough entropy

图像处理是获取信息的重要途径且被广泛地应用到军事、医学和交通等重要领域,图像分割在图像处理中占有重要地位。针对图像处理分割过程中的不确定性,为获取更加精确的图像分割效果,提出变精度最小平方粗糙熵和粒子群的图像单阈值分割算法。该单阈值分割算法用变精度粗糙集表示图像,以变精度最小平方粗糙熵求解最佳分割阈值,借助粒子群优化算法提高分割效率。实验表明,该单阈值分割算法明显优于最大平均信息熵法,且说明了变精度粗糙熵能够处理图像分割过程出现的不确定性。

Image processing is an important way to obtain information and is widely used in important fields such as military, medical and transportation fields. Image segmentation plays an important role in image processing. Aiming at the uncertainty in the process of image segmentation, and in order to obtain more accurate image segmentation results, we proposes a single-threshold image segmentation algorithm based on variable precision least square rough entropy and particle swarm optimization. It uses the variable precision rough set to represent the image, utilizes the variable precision least square rough entropy to solve the optimal segmentation threshold, and employs the particle swarm optimization to improve segmentation efficiency. Experimental results show that the single-threshold segmentation algorithm is superior to the maximum average entropy method, and demonstrates that variable precision rough entropy can settle the uncertainty problem in image segmentation process.