De-noising by thresholding operator adapted wavelets
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Donoho and Johnstone proposed a method from reconstructing an unknown smooth function u from noisy data u+ζ by translating the empirical wavelet coefficients of u+ζ towards zero. We consider the situation where the prior information on the unknown function u may not be the regularity of u but that of Ł u where Ł is a linear operator (such as a PDE or a graph Laplacian). We show that the approximation of u obtained by thresholding the gamblet (operator adapted wavelet) coefficients of u+ζ is near minimax optimal (up to a multiplicative constant), and with high probability, its energy norm (defined by the operator) is bounded by that of u up to a constant depending on the amplitude of the noise. Since gamblets can be computed in O(N polylog N) complexity and are localized both in space and eigenspace, the proposed method is of near-linear complexity and generalizable to non-homogeneous noise.
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