Chantas Giannis K, Galatsanos Nikolaos P, Woods Nathan A
Department of Computer Science, University of Ioannina, Ioannina, Greece.
IEEE Trans Image Process. 2007 Jul;16(7):1821-30. doi: 10.1109/tip.2007.896664.
In this paper, we propose a maximum a posteriori ramework for the super-resolution problem, i.e., reconstructing high-resolution images from shifted, rotated, low-resolution degraded observations. The main contributions of this work are two; first, the use of a new locally adaptive edge preserving prior for the super-resolution problem. Second an efficient two-step reconstruction methodology that includes first an initial registration using only the low-resolution degraded observations. This is followed by a fast iterative algorithm implemented in the discrete Fourier transform domain in which the restoration, interpolation and the registration subtasks of this problem are preformed simultaneously. We present examples with both synthetic and real data that demonstrate the advantages of the proposed framework.
在本文中，我们针对超分辨率问题提出了一种最大后验概率框架，即从平移、旋转后的低分辨率退化观测值重建高分辨率图像。这项工作的主要贡献有两点：第一，在超分辨率问题中使用了一种新的局部自适应边缘保持先验。第二，提出了一种高效的两步重建方法，该方法首先仅使用低分辨率退化观测值进行初始配准。接下来是在离散傅里叶变换域中实现的快速迭代算法，该算法同时执行此问题的恢复、插值和配准子任务。我们给出了合成数据和真实数据的示例，展示了所提出框架的优势。
