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- W2008454012 abstract "A computational Bayesian inversion model is demonstrated. It is discretization invariant, describes prior information using function spaces with a wavelet basis and promotes reconstructions that are sparse in the wavelet transform domain. The method makes use of the Besov space prior with p = 1, q = 1 and s = 1, which is related to the total variation prior. Numerical evidence is presented in the context of a one-dimensional deconvolution task, suggesting that edge-preserving and noise-robust reconstructions can be achieved consistently at various resolutions." @default.
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- W2008454012 date "2012-01-11" @default.
- W2008454012 modified "2023-10-11" @default.
- W2008454012 title "Sparsity-promoting Bayesian inversion" @default.
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- W2008454012 doi "https://doi.org/10.1088/0266-5611/28/2/025005" @default.
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