Image reconstruction from a Manhattan grid via piecewise plane fitting and Gaussian Markov random fields
Matthew A. Prelee, David L. Neuhoff, Thrasyvoulos N. Pappas · 2012
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.