Speaker
Carlo Schimd
(LAM)
Description
This lecture will cover the following topics:
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probability space, random variables and random vectors, probability distributions, expectation values, variance and covariance.
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Poisson noise, Gaussian noise, multiplicative noise: examples from electronic signals, electromagnetic spectra, images. Maximum likelihood estimators. Noise suppression and features detection: equalisation; linear and non-linear operations; filtering, smoothing, sharpening; Savitzky-Golay filter, bilateral filter, median filter, sigma-clipping.
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Classification: Principal component analysis (PCA), kernel PCA, dimensional reduction.
Primary author
Carlo Schimd
(LAM)
