BAYESIAN IMAGE RECONSTRUCTION: THE PIXON AND OPTIMAL IMAGE MODELING
BAYESIAN IMAGE RECONSTRUCTION: THE PIXON AND OPTIMAL IMAGE MODELING
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贝叶斯图像重建：像素和最优图像建模
DOI：
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发表时间：
1993
期刊：
影响因子：
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通讯作者：
R. Puetter
中科院分区：
文献类型：
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作者：
R. Piña;R. Puetter
本文介绍了最优图像模型最大残差似然法(OptMRL)用于图像重建。与最大熵方法(ME)一样，OptMRL是一种用于消除点扩展函数模糊的贝叶斯图像重建技术。像ME一样，OptMRL使用拟合优度标准(GOF)和“图像先验”，即量化图像先验概率的函数。然而，与通常在数据像素网格上重建图像的标准ME技术不同，OptMRL改变图像模型以找到表示iAge的最佳功能基础。在这一点上，我们的方法类似于Weir提出的多通道ME方法。在这篇文章中，我们展示了如何选择图像表示的最优基，并在这样做的过程中，发展了“像素点”的概念，它是一个广义的图像单元，从它构造这个基。通过允许图像和图像表示都是可变的，OptMRL方法大大增加了在其上优化图像的解空间的体积。因此，极大地增加了最终重建图像的可能性。对于拟合优度准则，OptMRL使用了Pina和Puetter之前介绍的最大残差概率分布。这种基于残差空间自相关的GOF概率分布具有确保图像重建残差在空间上不相关的优点。
In this paper we describe the Optimal Image Model, Maximum Residual LIkelihood method (OptMRL) for image reconstruction. OptMRL, like maximum entropy mehtods (ME), is a Bayesian image reconstruction technique for removing point spread function blurring. Like ME, OptMRL uses both a goodness of fit criterion (GOF) and an "image prior," i.e., a function which quantifies the a priori probability of the image. However, unlike standard ME techniques which typically reconstruce the image on the data pixel grid, OptMRL varies the image model in order to find the optimal functional basis with which to represent the iage. In this regard, our method is similar to the multi-channel ME methods proposed by Weir. In this paper, we show how an optimal basis for image representation can be selected and in doing so, develop the concept of the "pixon" which is a generalized image cell from which this basis is constructed. By allowing both the image and the image representation to be variable, the OptMRL method greatly increases the volume of solution space over which the image is optimized. Hence the likelihood of the final reconstructed image is greatly increased. For the goodness of fit criterion, OptMRL uses the Maximum Residual Likelihood probability distribution introduced previously by Pina and Puetter. This GOF probability distribution, which is based on the spatial autocorrelation of the residuals, has the advantage that it ensures spatially uncorrelated image reconstruction residuals.