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首页> 外文会议>IEEE International Conference on Acoustics, Speech and Signal Processing >A GRADIENT-LIKE VARIATIONAL BAYESIAN APPROACH FOR JOINT IMAGE SUPER-RESOLUTION AND SOURCE SEPARATION, APPLICATION TO ASTROPHYSICAL MAP-MAKING
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A GRADIENT-LIKE VARIATIONAL BAYESIAN APPROACH FOR JOINT IMAGE SUPER-RESOLUTION AND SOURCE SEPARATION, APPLICATION TO ASTROPHYSICAL MAP-MAKING

机译：一种梯度样变分别贝叶斯方法，用于联合图像超分辨率和源分离，应用于天体物理地图制作

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In this work, a new unsupervised Bayesian method for joint image super-resolution and component separation is introduced. More precisely, we are interested in super-resolution for astrophysical map-making and separation between extended and point emissions. This is tackled as an inverse problem in a Bayesian framework, where a Markovian model is used as a prior for the extended emission and a student's t-distribution is attributed for the point sources component. All model and noise parameters are unknown, therefore we chose to estimate them jointly with the images. Nevertheless, both Joint Maximum A Posteriori (JMAP) and Posterior Mean (PM) estimators are intractable. Hence, we propose to approximate the true posterior by free-form separable distribution using a gradient-like variational Bayesian approach, which allows a joint update of the shape parameters of the approximating marginals. Applications on simulated and real datasets, obtained from Herschel space observatory, show a good quality of reconstruction. Furthermore, compared to conventional methods, our method gives a higher resolution while conserving photometery and reducing noise.
机译：在这项工作中，引入了一种新的无监督贝叶斯方法，用于联合图像超分辨率和组分分离。更准确地说，我们对天体物理地图制作和分离的超级分辨率感兴趣，延伸和点排放之间的分离。这被视为贝叶斯框架中的逆问题，其中Markovian模型用作延长发射之前，并且学生的T分布归因于点源分量。所有模型和噪声参数都未知，因此我们选择与图像共同估计它们。然而，联合最大值是后验（JMAP）和后平均值（PM）估计器是棘手的。因此，我们建议使用使用梯度的变形贝叶斯方法来近似于自由形状可分离分布的真实后部，这允许接合更新近似边缘的形状参数。从Herschel空间天文台获得的模拟和实时数据集上的应用，表现出良好的重建品质。此外，与传统方法相比，我们的方法在节省光度和降低噪声的同时提供更高的分辨率。

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