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Noise removal algorithms
Transcript of Noise removal algorithms
OPIS METODY KTORA CHCEMY WYBRAC
DENOISING IMAGES USING STATISTICAL TECHNIQUES
Manuel Lucena Córdoba
Leopoldo Gómez Castillo
DIVISION OF LABOUR
Principal component analysis
Involves a mathematical procedure
that transforms a number of (possibly)
correlated variables intoa (smaller)
number of uncorrelated variables
robust statistic is resistant to errors in the results, produced by deviations from assumptions (e.g., of normality). This means that if the assumptions are only approximately met, the robust estimator will still have a reasonable efficiency, and reasonably small bias, as well as being asymptotically unbiased, meaning having a bias tending towards 0 as the sample size tends towards infinity.
THANK YOU FOR YOUR ATTENTION!
See you on November 12th
Image denoising is a procedure in digital image processing aiming at the removal of noise, which may corrupt an image during its acquisition or transmission, while retaining its quality. The
presence of noise not only produces undesirable visual quality but also lowers the visibility of
low contrast objects. Noise removal is essential in medical
fine details that
may be hidden
in the data.
environment: Matlab R2009b
called principal components.
To discover or to reduce the dimensionality of the data set.
To identify new meaningful underlying variables.
Main objectives of principal component analysis :
case study of ICA algorithm