Orieux François, Giovannelli Jean-François, Rodet Thomas
Laboratoire des Signaux et Systèmes (CNRS-SUPELEC-Univ. Paris-Sud 11), SUPELEC, Plateau de Moulon,3 rue Joliot-Curie, 91 192 Gif-sur-Yvette, France.
J Opt Soc Am A Opt Image Sci Vis. 2010 Jul 1;27(7):1593-607. doi: 10.1364/JOSAA.27.001593.
This paper tackles the problem of image deconvolution with joint estimation of point spread function (PSF) parameters and hyperparameters. Within a Bayesian framework, the solution is inferred via a global a posteriori law for unknown parameters and object. The estimate is chosen as the posterior mean, numerically calculated by means of a Monte Carlo Markov chain algorithm. The estimates are efficiently computed in the Fourier domain, and the effectiveness of the method is shown on simulated examples. Results show precise estimates for PSF parameters and hyperparameters as well as precise image estimates including restoration of high frequencies and spatial details, within a global and coherent approach.
本文研究了在联合估计点扩散函数（PSF）参数和超参数的情况下的图像去卷积问题。在贝叶斯框架内，通过针对未知参数和目标的全局后验法则来推断解决方案。估计值被选为后验均值，通过蒙特卡罗马尔可夫链算法进行数值计算。估计值在傅里叶域中高效计算，并在模拟示例中展示了该方法的有效性。结果表明，在全局且连贯的方法中，对于PSF参数和超参数以及包括高频恢复和空间细节的精确图像估计都能得到精确的估计。
