Ghael, SadeepSayeed, Akbar M.Baraniuk, Richard G.2007-10-312007-10-311997-07-011997-07-01S. Ghael, A. M. Sayeed and R. G. Baraniuk, "Improved Wavelet Denoising via Empirical Wiener Filtering," 1997.https://hdl.handle.net/1911/19895Conference PaperWavelet shrinkage is a signal estimation technique that exploits the remarkable abilities of the wavelet transform for signal compression. Wavelet shrinkage using thresholding is asymptotically optimal in a minimax mean-square error (MSE) sense over a variety of smoothness spaces. However, for any given signal, the MSE-optimal processing is achieved by the Wiener filter, which delivers substantially improved performance. In this paper, we develop a new algorithm for wavelet denoising that uses a wavelet shrinkage estimate as a means to design a wavelet-domain Wiener filter. The shrinkage estimate indirectly yields an estimate of the signal subspace that is leveraged into the design of the filter. A peculiar aspect of the algorithm is its use of two wavelet bases: one for the design of the empirical Wiener filter and one for its application. Simulation results show up to a factor of 2 improvement in MSE over wavelet shrinkage, with a corresponding improvement in visual quality of the estimate. Simulations also yield a remarkable observation: whereas shrinkage estimates typically improve performance by trading bias for variance or vice versa, the proposed scheme typically decreases both bias and variance compared to wavelet shrinkage.engwaveletsdenoisingestimationWiener filtersubspaceWavelet based Signal/Image ProcessingConference paperwaveletsdenoisingestimationWiener filtersubspacehttp://dx.doi.org/10.1117/12.292799