-MINIMAX WAVELET SHRINKAGE: A ROBUST INCORPORATION OF INFORMATION ABOUT ENERGY OF A SIGNAL IN DENOISING APPLICATIONS Academic Article uri icon

abstract

  • In this paper we propose a method for wavelet-filtering of noisy signals when prior information about the L2-energy of the signal of interest is available. Assuming the independence model, according to which the wavelet coefficients are treated individually, we propose a level dependent shrinkage rule that turns out to be the Γ-minimax rule for a suitable class, say Γ, of realistic priors on the wavelet coefficients. The proposed methodology is particularly well suited for denoising tasks where signal-to-noise ratio is low, and it is illustrated on a battery of standard test functions. Performance comparisons with some others methods existing in the literature are provided. An example in atomic force microscopy (AFM) is also discussed.

published proceedings

  • Statistica Sinica

author list (cited authors)

  • Angelini, C., & Vidakovic, B.

complete list of authors

  • Angelini, C||Vidakovic, B