基于信息论中最大熵原理 ,提出了一种等概率场下的自动选取阈值的新方法 .该方法是首先通过图象灰度直方图信息 ,并利用 Shannon熵中等概率场具有最大熵的基本性质来确定阈值 ,然后将图象划分为等概率的子块 ,进而给出了该算法的理论推导和算法的具体实现步骤 .与通常的基于熵理论自动获取图象阈值的方法相比较 ,该方法直观、简便、求解稳定 ,且易于实现 .同时该方法克服了常用方法在阈值求取时 ,出现的诸如计算量大、计算效率低等不足等问题 ,故能够迅速地获得图象的阈值 .对比实验的结果 ,也说明了该方法的快速性、有效性、可靠性和稳定性

Image processing has to deal with many information of an image. Gray histogram can contain a lot of image information. Maximum entropy theorem of Information Theory is one of the useful tools to treat with this kind of information. There are several formulas for computing the maximum entropy. But almost of the existing formulas have some common disadvantages, such as expensively computing and more complex algorithm realizing. In order to overcome these weaknesses of the existing entropy formulas, in this paper we define a new approach to entropy, and use it to automatically select thresholds of the image. It bases on one of Shannon entropy's basic properties that the equivalent probability distributing has maximum entropy to get the image thresholds. And by this way, we can segment an image into several equivalent probability sub parts. This new method has some advantages, such as simplified, stabilized and easily realized comparing with some traditional entropy methods. At the same time, it can get image thresholds quickly. We have employed the newly proposed approach to perform image enhancement, segmentation and thresholding, and obtained satisfactory results.