4 February 2011 Wiener crosses borders: interpolation based on second order models
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Proceedings Volume 7870, Image Processing: Algorithms and Systems IX; 78700L (2011); doi: 10.1117/12.871198
Event: IS&T/SPIE Electronic Imaging, 2011, San Francisco Airport, California, United States
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 (4 February 2011); doi: 10.1117/12.871198; https://doi.org/10.1117/12.871198
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KEYWORDS
Linear filtering

Image processing

Mathematical modeling

Signal processing

Data modeling

Image filtering

Electronic filtering

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