为解决现有算法无法有效处理被多种噪声干扰的图像阈值分割问题,提出了3维最小误差阈值法。该方法充分考虑图像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;同时为了提高算法的处理速度,提出基于分解的快速实现方法,将3维阈值的求解分解成3个1维阈值的求解,其时间复杂度降为O(L),空间复杂度降为S(L)。实验结果分析表明,在不同噪声环境及非均匀光照条件下,尤其对多种噪声干扰的图像,与现有方法相比,本文算法均取得了更好的分割效果。

Since existing algorithms cannot solve the threshold segmentation problem of mixed noise images a 3D minimum error threshold algorithm is proposed in this paper. Using gray distribution information of pixels and relevant information of neighboring pixels, it combines image gray, mean and median values to construct a 3D observation space, and then defines a 3D optimal threshold discriminant based on the relative entropy. Furthermore, in order to improve its processing speed, the fast calculation method based on decomposition is proposed. It calculates three 1D optimal thresholds, instead of one 3D optimal threshold. Its time complexity is reduced to O(L), and space cost is reduced to S(L). Experimental results show that the proposed algorithm outperforms those 2D threshold methods for different types of noised image and non-uniform illuminating images. Especially for mixed noise image, its advantage is more obvious.