融合模糊聚类的蚁群图像增强算法

为提高边缘检测精准度，保证图片分割后效率和效果，本文提出一种基于融合模糊聚类的蚁群图像增强算法。该算法利用分量灰度值、灰度梯度值和领域特征值进行图像特征提取，得到特征灰度图；然后使用模糊聚类算法对区域蚂蚁进行聚类以提高收敛速度；再采用蚁群算法进行图像边缘检测，检测过程中，使用路径选择策略对蚁群进行有序搜索，提高搜索效率，又根据信息素更新策略进行最优路径信息交流，以达到边缘点提取与检索目的；最后将检索所得灰度边缘图与原图进行重合，得到图像增强效果。实验结果表明，该改进算法在检索时间方面相较于传统蚁群算法提高了20.7%；在精度方面提高了14.8%，图片分割效果更好，纹理更清晰。

Ant Colony Image Enhancement Algorithm for Fusion Fuzzy Clustering

To improve the accuracy of edge detection and ensure the efficiency and effect of image segmentation, an ant colony image enhancement algorithm based on fuzzy clustering is proposed on the basis of ant colony algorithm. The algorithm uses component grayscale value, grayscale gradient value and domain eigenvalue to extract image features, then uses fuzzy clustering to specify the clustering center to improve the convergence speed, then uses the ant colony algorithm to realize the image edge detection, in the process of detection, using the path selection strategy to search the ant colony in order to improve the search efficiency, According to the pheromone update strategy, the optimal path information exchange is realized in order to achieve the purpose of edge point extraction and retrieval, and finally the processed grayscale edge graph coincides with the original picture to realize the effect of image enhancement. Experimental results show that the improved algorithm improves the retrieval time compared with the traditional ant colony algorithm by 20.7%, improves the accuracy by 14.8%, and the texture is clearer in the aspect of image segmentation.