3 February 2011 Wiener crosses borders: interpolation based on second order models
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Abstract
Interpolation of signals (arbitrary dimension, here: 2D images) with missing data points is addressed from a statistical point of view. We present a general framework for which a Wiener-style MMSE estimator can be seamlessly adapted to deal with problems such as image interpolation (inpainting), reconstruction from sparse samples, and image extrapolation. The proposed method gives a precise answer on a) how arbitrary can linear filters can be applied to initially incomplete signals and b) shows the definite way to extend images beyond theirs borders such that no size reduction occurs if a linear filter/operator is to be applied to the image.
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Alvaro Guevara, Rudolf Mester, "Wiener crosses borders: interpolation based on second order models", Proc. SPIE 7870, Image Processing: Algorithms and Systems IX, 78700L (3 February 2011); doi: 10.1117/12.871198; https://doi.org/10.1117/12.871198
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