IEEE Trans Image Process. 2011 Jul;20(7):2078-83. doi: 10.1109/TIP.2011.2105881. Epub 2011 Jan 13.
A new algorithm for maximum-likelihood blind image restoration is presented in this paper. It is obtained by modeling the original image and the additive noise as multivariate Gaussian processes with unknown covariance matrices. The blurring process is specified by its point spread function, which is also unknown. Estimations of the original image and the blur are derived by alternating minimization of the Kullback-Leibler divergence between a model family of probability distributions defined using the linear image degradation model and a desired family of probability distributions constrained to be concentrated on the observed data. The algorithm presents the advantage to provide closed form expressions for the parameters to be updated and to converge only after few iterations. A simulation example that illustrates the effectiveness of the proposed algorithm is presented.
本文提出了一种新的最大似然盲图像恢复算法。它通过将原始图像和附加噪声建模为具有未知协方差矩阵的多元高斯过程来获得。模糊过程由其点扩散函数指定，该函数也是未知的。通过交替最小化使用线性图像退化模型定义的概率分布模型族与约束为集中在观测数据上的期望概率分布模型族之间的 Kullback-Leibler 散度，导出了原始图像和模糊的估计值。该算法的优点在于提供了要更新的参数的封闭形式表达式，并仅在几次迭代后收敛。给出了一个模拟示例来说明所提出算法的有效性。
