Title :
Minimum risk thresholds for data with heavy noise
Author_Institution :
Dept. of Math. & Comput. Sci., TU Eindhoven, Netherlands
fDate :
5/1/2006 12:00:00 AM
Abstract :
In the estimation of data with many zeros (sparse data), such as wavelet coefficients, thresholding is a common technique. This letter investigates the behavior of the minimum risk threshold for large values of the noise standard deviation. It finds that the threshold depends quadratically on the noise standard deviation. The relevance of this result is situated in the context of both Bayesian and universal thresholding.
Keywords :
Bayes methods; image denoising; Bayesian thresholding; data estimation; minimum risk threshold; noise standard deviation; universal thresholding; Additives; Analysis of variance; Bayesian methods; Computer science; Gene expression; Image denoising; Mathematics; Random variables; Wavelet coefficients; Bayes; threshold; wavelet;
Journal_Title :
Signal Processing Letters, IEEE
DOI :
10.1109/LSP.2006.870355
