4 February 2011 Wiener crosses borders: interpolation based on second order models
Author Affiliations +
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
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.
© (2011) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
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

Linear filtering

Image processing

Mathematical modeling

Signal processing

Data modeling

Image filtering

Electronic filtering


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