New methods for signal and image processing by information theoretic divergence

1.本文利用Renyi的a-阶熵给出了一类统一的随机过程的等价带宽。这对于评价随机过程的随机性是很有用的.我们研究了图像的全局阈值。在传统的一步法基础上，提出了两步法的新思想。在该方法的第一步中，您估计灰度变量服从的最佳模型概率分布，在第二步中，您决定对图像进行二值化的最佳阈值。本文提出了一种新的面向二值化的模型分布拟合方法，引入类标签作为隐变量，用信息论互信息测度代替传统的发散测度。仿真实验表明，我们的方法是非常强大的。

1. We proposed a unified class of equivalent bandwidths of a random process by using Renyi's a-order entropy. It is useful to evaluate randomness of a random process.2. We investigated global thresholding of a image. In addition to traditional one-step methods, we proposed a new idea of the two-steps method. In the first step of the method, you estimate the optimum model probability distribution that the grey-level variable obeys and in the second step you decide the optimum threshold to binarize the image. We propose a new binarization-oriented method for model distriobution fitting where we introduce class labels as hiden-variable and, instead of the traditional divergence measure, information theoretic mutual information measure is used. Simulation experiments shows our methodes are very powerful.