基于清晰度评价的自适应阈值图像分割法

Adaptive Threshold Image Segmentation Based on Definition Evaluation

阈值法是一种被广泛使用的图像分割方法.本文从图像中信息的变化情况出发,提出一种基于图像清晰度评价的新颖的自适应阈值分割方法.该方法采用清晰度评价函数作为阈值化后图像内灰度相似性变化的度量方法,通过反复迭代并结合皮尔逊相关性直至找到最佳的分割阈值.通过多组图像数据尤其低对比度图像,包括钢板表面轻微缺陷等图像进行了测试对比.结果表明:相比传统阈值分割方法及其改进算法,在低对比度图像的处理上,本文方法能够自适应地准确找到合理阈值,具有优异的图像分割性能.

Threshold is a widely used method for image segmentation.With the variance of the information in the image,this paper proposed a novel adaptive threshold segmentation method based on image definition evaluation.This method uses the definition evaluation function as a measure of the gray similarity change in the image after thresholding.Repeated iteration and Pearson correlation were combined until the optimal segmentation threshold was found.Test comparisons were performed using multiple sets of image data,especially low-contrast images,such as slight defects on the steel surface.The results showed that compared with the traditional threshold segmentation method and its improved algorithm,in the processing of low-contrast images,the proposed method can adaptively and accurately find a reasonable threshold value,and has an excellent performance of image segmentation.