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- W1496090722 abstract "In a series of papers, Donoho and Johnstone develop a powerful theory based on wavelets for extracting non-smooth signals from noisy data. Several nonlinear smoothing algorithms are presented which provide high performance for removing Gaussian noise from a wide range of spatially inhomogeneous signals. However, like other methods based on the linear wavelet transform, these algorithms are very sensitive to certain types of non-Gaussian noise, such as outliers. In this paper, we develop outlier resistant wavelet transforms. In these transforms, outliers and outlier patches are localized to just a few scales. By using the outlier resistant wavelet transform, we improve upon the Donoho and Johnstone nonlinear signal extraction methods. The outlier resistant wavelet algorithms are included with the 'S+WAVELETS' object-oriented toolkit for wavelet analysis." @default.
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- W1496090722 date "1994-03-15" @default.
- W1496090722 modified "2023-10-13" @default.
- W1496090722 title "Denoising and robust nonlinear wavelet analysis" @default.
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- W1496090722 doi "https://doi.org/10.1117/12.170036" @default.
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