Fundamentals of digital image processing
Fundamentals of digital image processing
Fundamentals of statistical signal processing: estimation theory
Fundamentals of statistical signal processing: estimation theory
Image Thresholding by Indicator Kriging
IEEE Transactions on Pattern Analysis and Machine Intelligence
Nonlinear prediction by kriging, with application to noise cancellation
Signal Processing
Time Series Analysis: Forecasting and Control
Time Series Analysis: Forecasting and Control
Nonlinear filtering by kriging, with application to system inversion
ICASSP '99 Proceedings of the Acoustics, Speech, and Signal Processing, 1999. on 1999 IEEE International Conference - Volume 03
The kriging update model and recursive space-time functionestimation
IEEE Transactions on Signal Processing
Prediction from off-grid samples using continuous normalized convolution
Signal Processing
Computational prediction models for cancer classification using mass spectrometry data
International Journal of Data Mining and Bioinformatics
Computationally efficient spatial interpolators based on spartan spatial random fields
IEEE Transactions on Signal Processing
Wiener channel smoothing: robust wiener filtering of images
PR'05 Proceedings of the 27th DAGM conference on Pattern Recognition
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The Wiener filter is the well-known solution for linear minimum mean square error (LMMSE) signal estimation. This filter assumes the mean to be known and usually constant. On the other hand, the Kriging filter is an incremental theory, developed within the Geostatistical community, with respect to that of Wiener filters. The extension relies on adopting a parametric model for the mean (usually a polynomial). The goal of this paper is twofold. First, it is intended as a comprehensive treatment of the Kriging approach from a signal processing perspective, with previous uses of Kriging in signal processing being extended. Second, we are deriving a general methodology for FIR filter design, including any situation where an optimal FIR estimator from possibly incomplete and/or noisy data is needed. A proof of concept on a theoretical covariance model and selected examples on interpolation, approximation and filtering on real-world images illustrate the performance of the method.