Faculty of Physics, Department of Electronics, Computers, Telecommunications and Control, National and Kapodistrian University of Athens, Panepistimiopolis, Zografos, 15784 Athens, Greece.
IEEE Trans Image Process. 2010 Jun;19(6):1451-64. doi: 10.1109/TIP.2010.2042115. Epub 2010 Feb 2.
Super-resolution (SR) is the term used to define the process of estimating a high-resolution (HR) image or a set of HR images from a set of low-resolution (LR) observations. In this paper we propose a class of SR algorithms based on the maximum a posteriori (MAP) framework. These algorithms utilize a new multichannel image prior model, along with the state-of-the-art single channel image prior and observation models. A hierarchical (two-level) Gaussian nonstationary version of the multichannel prior is also defined and utilized within the same framework. Numerical experiments comparing the proposed algorithms among themselves and with other algorithms in the literature, demonstrate the advantages of the adopted multichannel approach.
超分辨率（SR）是指从一组低分辨率（LR）观测中估计高分辨率（HR）图像或一组 HR 图像的过程。在本文中，我们提出了一类基于最大后验（MAP）框架的 SR 算法。这些算法利用了新的多通道图像先验模型，以及最先进的单通道图像先验和观测模型。还在同一框架内定义并利用了多通道先验的分层（两级）高斯非平稳版本。在数值实验中，将所提出的算法相互之间以及与文献中的其他算法进行了比较，证明了所采用的多通道方法的优势。
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