摘要
针对现有算法无法有效解决混合噪声图像的阈值分割的问题,该文提出3维最小误差阈值法。该方法充分考虑图像像元点的灰度分布信息及像元点之间的灰度相关信息,结合图像灰度、均值和中值信息,构造出3维观测空间。然后基于相对熵定义出3维最佳阈值判别式。为了提高该算法的处理速度,给出相应的快速递推算法,其时间复杂度为O(L3)。实验结果表明,在不同噪声环境及非均匀光照条件下,尤其对混合噪声图像,与现有方法相比,文中算法均取得了更好的分割效果。
The threshold segmentation of mixed noise image can not be solved by existing algorithms efficiently. A 3D minimum error thresholding algorithm is proposed. Using gray distribution information of pixels and relevant information of neighboring pixels, it combines information of image gray, mean and median to construct a three-dimensional observation space, and then defines a 3D optimal threshold discriminant based on the relative entropy. ~rthermore, in order to improve its processing speed, the fast recursive formulas are also given. Its time complexity is O（L3）. Experimental results show that the proposed algorithm outperforms those 2D thresholding methods not only for different types of noised image, but also for non-uniform illuminating images. Especially for mixed noise image, its advantage is more obvious.
出处
《电子与信息学报》
EI
CSCD
北大核心
2013年第9期2073-2080,共8页
Journal of Electronics & Information Technology
基金
国家自然科学基金重点项目(61134002)
国家自然科学基金(60971103)
中央高校基本科研业务费专项基金(SWJTU11BR026)资助课题
关键词
图像处理
阈值分割
最小误差阈值法
3维灰度直方图
相对熵
Image processing
Threshold segmentation
Minimum error thresholding method
Three-dimensionalgray-level histogram
Relative entropy
