变分贝叶斯估计图像滤波去噪算法

Image Filtering and Denoising Algorithm Based on Variable Bayes Estimating

图像在拍摄获取、存储和传输等过程中,由于操作、光线等会造成各种噪音。变分贝叶斯估计图像滤波去噪算法,是通过平均场理论将隐变量的后验按维度展开得到计算框架,并按维度迭代更新估计结果至算法收敛,引入新的隐变量,给出过程噪声后验分布。输入向量X(n)和期望响应d(n)被用来计算估计误差e(n),并利用此误差信号构造一个自适应算法的性能函数,随着数据输入的变化,自适应地更新此性能函数,并且不断使其最小化。在此过程中不断地更新调整滤波器的滤波参数,使得这个参数能在最小化性能函数所使用的准则下最优,从而达到滤波效果。首先,定义贝叶斯估计图像滤波模型;然后,利用贝叶斯后验概率分布推导、计算真实分布和近似分布之间的Kullback-Leibler距离;最后,以迭代递推方式估计目标状态,提高状态估计精度,实现图像滤波去噪。大量实验表明该算法滤波去噪效果明显,能最大程度地保护图像细节。

In the process of image acquisition,storage and transmission,due to the operation,light and so on,various noises will be caused.The image filtering and denoising algorithm based on the variable Bayes estimating is to expand the posterior of hidden variables according to the dimension by the mean field theory,update the estimating results according to the dimension iteratively to the convergence of the algorithm,and give the posterior distribution of process noise by introducing new hidden variables.The input vector X(n)and the expected response d(n)are used to calculate the estimation error e(n),which is used to construct the performance function of the adaptive algorithm.With the change of data input,the performance function is updated adaptively and minimized constantly.In this process,the filter parameters are constantly updated and adjusted,so that they can be optimized under the criterion of minimizing the performance function,so as to achieve the filtering effect.Firstly,the model of Bayes estimating is defined.Then,the Kullback-Leibler distance between the real distribution and the approximate distribution is derived and calculated by using the Bayes posterior probability distribution.Finally,the target state is estimated by iterative recursive method to improve the state estimation accuracy and achieve image filtering and denoising.A large number of experiments show that the filtering and denoising effect of the algorithm is obvious,and it can protect image details to the greatest extent.