摘要： 图像噪声容易引起图像误分割，而常用阈值选取方法仅依赖于图像直方图的概率信息，未直接考虑图像中类内灰度分布的均匀性。为此，提出一种修正三维直方图和分解处理灰度熵的图像分割算法。分析图像的噪声对其邻域灰度造成的影响，通过修正三维直方图来减弱噪声干扰，给出三维灰度熵阈值的选取公式，并将三维灰度熵分解至一维进行处理，使计算复杂度由O(L3)降为O(L)。实验结果表明，与二维最大熵斜分法、二维交叉熵递推法、降维三维Otsu法相比，该算法抗噪性能更强、分割效果更好，同时能使运算时间缩短10%以上。

关键词: 图像分割, 阈值选取, 图像噪声, 三维直方图, 分解处理, 灰度熵

Abstract: Aiming at the problem of inaccurating image segmentation caused by image noise and the common threshold selection methods which only rely on the probabilistic information from the image histogram is without directly thinking of the uniformity of the image inter-class gray distribution, a threshold selection algorithm based on a three-dimensional histogram correction and gray entropy de- composition is proposed. It analyzes the influence of image noise to the gray of pixel’s neighborhood region, and reduces the noise interference by modifying the three-dimensional histogram. A formula of threshold selection based on three-dimensional gray entropy is presented, and the dimension of gray entropy is decomposed to one dimension, which makes the computation complexity reduced from O(L3) to O(L). Experimental results show that, compared with two-dimensional maximum entropy algorithm based on oblique segmen- tation, two-dimensional cross entropy algorithm based on recursion and three-dimensional Otsu algorithm based on dimension reduction, the presented algorithm has better anti-noise performance, visual quality and the operation time is reduced by about 10% at least.

Key words: image segmentation, threshold selection, image noise, three-dimensional histogram, decomposition processing, gray entropy

中图分类号: 

• TP391