Median Modified Wiener Filter for nonlinear adaptive spatial denoising of protein NMR multidimensional spectra
Permanent link to this recordhttp://hdl.handle.net/10754/344037
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AbstractDenoising multidimensional NMR-spectra is a fundamental step in NMR protein structure determination. The state-of-the-art method uses wavelet-denoising, which may suffer when applied to non-stationary signals affected by Gaussian-white-noise mixed with strong impulsive artifacts, like those in multi-dimensional NMR-spectra. Regrettably, Wavelet's performance depends on a combinatorial search of wavelet shapes and parameters; and multi-dimensional extension of wavelet-denoising is highly non-trivial, which hampers its application to multidimensional NMR-spectra. Here, we endorse a diverse philosophy of denoising NMR-spectra: less is more! We consider spatial filters that have only one parameter to tune: the window-size. We propose, for the first time, the 3D extension of the median-modified-Wiener-filter (MMWF), an adaptive variant of the median-filter, and also its novel variation named MMWF*. We test the proposed filters and the Wiener-filter, an adaptive variant of the mean-filter, on a benchmark set that contains 16 two-dimensional and three-dimensional NMR-spectra extracted from eight proteins. Our results demonstrate that the adaptive spatial filters significantly outperform their non-adaptive versions. The performance of the new MMWF* on 2D/3D-spectra is even better than wavelet-denoising. Noticeably, MMWF* produces stable high performance almost invariant for diverse window-size settings: this signifies a consistent advantage in the implementation of automatic pipelines for protein NMR-spectra analysis.
CitationMedian Modified Wiener Filter for nonlinear adaptive spatial denoising of protein NMR multidimensional spectra 2015, 5:8017 Scientific Reports
SponsorsThis work was supported by the independent group leader starting grant of the Technische Universita¨t Dresden (TUD), and Award No. GRP-CF-2011-19-P-Gao-Huang and a GMSV-OCRF award from King Abdullah University of Science and Technology (KAUST).
PublisherNature Publishing Group
PubMed Central IDPMC4306135
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