13 November 2003 Iterative projective wavelet methods for denoising
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Wavelet thresholding is a powerful tool for denoising images and other signals with sharp discontinuities. Using different wavelet bases gives different results, and since the wavelet transform is not time-invariant, thresholding various shifts of the signal is one way to use different wavelet bases. This paper describes several denoising methods that apply wavelet thresholding or variations on wavelet thresholding recursively. (We previously termed one of these methods "recursive cycle spinning.") These methods are compared experimentally for denoising piecewise polynomial signals. Though similar, the methods differ in computational complexity, convergence speed, and sensitivity to threshold selection.
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Alyson K. Fletcher, Vivek K Goyal, Kannan Ramchandran, "Iterative projective wavelet methods for denoising", Proc. SPIE 5207, Wavelets: Applications in Signal and Image Processing X, (13 November 2003); doi: 10.1117/12.507250; https://doi.org/10.1117/12.507250


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