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首页> 外文会议>IEEE International Conference on Image Processing;ICIP 2012 >Image reconstruction from a Manhattan grid via piecewise plane fitting and Gaussian Markov random fields
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Image reconstruction from a Manhattan grid via piecewise plane fitting and Gaussian Markov random fields

机译：通过分段平面拟合和高斯马尔可夫随机场从曼哈顿网格重构图像

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This paper builds upon previous work for image reconstruction problems in which samples are taken on evenly spaced rows and columns, i.e., a Manhattan grid. A new reconstruction method is proposed that uses three steps to interpolate the interior of each block under the model that an image can be decomposed into piecewise planar regions plus noise. First, the K-planes algorithm is developed in order to fit several planes to the observed pixel values on the border. Second, one of theK planes is assigned to each pixel of the block interior, by a process of partitioning the block with polygons, thereby creating a piecewise planar approximation. Third, the interior pixels are interpolated by modeling them as a Gauss Markov random field whose mean is the piecewise planar approximation just obtained. The new method is shown to improve significantly upon previous methods, especially in the preservation of “soft” image edges.
机译：本文基于先前针对图像重建问题的工作，其中在均匀间隔的行和列（即曼哈顿网格）上采样。提出了一种新的重建方法，该方法使用三个步骤对模型下每个块的内部进行插值，从而可以将图像分解为分段平面区域和噪声。首先，开发了K平面算法，以使多个平面适合边界上观察到的像素值。第二，通过用多边形划分块的过程，将K个平面之一分配给块内部的每个像素，从而产生分段的平面近似。第三，通过将内部像素建模为高斯马尔可夫随机场进行内插，其平均值是刚获得的分段平面近似。结果表明，新方法比以前的方法有显着改进，尤其是在保留“软”图像边缘方面。

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