Threshold Selection in Transform Shrinkage

The transform shrinkage paradigm is reviewed, of which wavelet denoising is a key example, with a focus on the blockwise approach to processing of transform coeffients. Thresholding approaches are surveyed, with special emphasis is placed on an empirical Bayes approach, which promises to adapt well to the demands of both ’dense’ and ’sparse’ signals. Since the author has no significant experience with problems in astronomy, discussion and examples (denoising of signals, images and decon volution) are alas generic.

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