Minimum risk thresholds for data with heavy noise

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.