标题： 正则化反演中超参数和仪器参数的估计。 SPIRE/Herschel地图制作的说明 显示英文标题

标题： Estimating hyperparameters and instrument parameters in regularized inversion. Illustration for SPIRE/Herschel map making

摘要： 我们描述了用于图像重建的正则化方法，并重点关注超参数和仪器参数估计的问题，即无监督和近视问题。 我们开发了一个基于观测值下所有未知量的\post 密度的贝叶斯框架。 该密度通过基于Gibbs循环的马尔可夫链蒙特卡洛采样技术进行探索，并包括一个Metropolis-Hastings步骤。 数值评估依赖于赫歇尔望远镜的SPIRE仪器。 使用模拟和真实观测数据，我们展示了超参数和仪器参数能够被正确估计，这为天文学中的成像研究开辟了许多前景。 显示英文摘要

摘要： We describe regularized methods for image reconstruction and focus on the question of hyperparameter and instrument parameter estimation, i.e. unsupervised and myopic problems. We developed a Bayesian framework that is based on the \post density for all unknown quantities, given the observations. This density is explored by a Markov Chain Monte-Carlo sampling technique based on a Gibbs loop and including a Metropolis-Hastings step. The numerical evaluation relies on the SPIRE instrument of the Herschel observatory. Using simulated and real observations, we show that the hyperparameters and instrument parameters are correctly estimated, which opens up many perspectives for imaging in astrophysics.