使用Gauss-Markov-Potts先验模型和变分贝叶斯计算的联合图像恢复和分割：技术细节
Joint Image Restoration and Segmentation using Gauss-Markov-Potts Prior Models and Variational Bayesian Computation: Technical Details
摘要：
本文提出了一种同时恢复和分割由已知的点扩散函数和加性白噪声退化的图像的方法。为此，我们提出了一个联合贝叶斯估计框架，其中一个家庭的非齐次高斯-马尔可夫场与波茨区域标签模型被选择作为先验的图像。由于联合最大后验估计和后验均值估计都不容易处理，因此图像的联合后验律、图像的分割和所有超参数都使用变分贝叶斯技术由可分离的概率律来近似。这产生了一个已知的概率规律的后验与相互依赖的成形参数，其目的是提高收敛速度的估计相比，随机抽样为基础的估计。主要工作在[1]中给出，而变分计算的技术细节在本文中给出。
英文摘要：
We propose a method to restore and to segment simultaneously images degraded by a known point spread function (PSF) and additive white noise. For this purpose, we propose a joint Bayesian estimation framework, where a family of non-homogeneous Gauss-Markov fields with Potts region labels models are chosen to serve as priors for images. Since neither the joint maximum a posteriori estimator nor posterior mean one are tractable, the joint posterior law of the image, its segmentation and all the hyper-parameters, is approximated by a separable probability laws using the Variational Bayes technique. This yields a known probability laws of the posterior with mutually dependent shaping parameter, which aims to enhance the convergence speed of the estimator compared to stochastic sampling based estimator. The main work is description is given in [1], while technical details of the variational calculations are presented in the current paper.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
