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25 June 1999 Bayesian wavelet-based image estimation using noninformative priors
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Abstract
The sparseness and decorrelation properties of the discrete wavelet transform have been exploited to develop powerful denoising methods. Most schemes use arbitrary thresholding nonlinearities with ad hoc parameters, or employ computationally expensive adaptive procedures. We overcome these deficiencies with a new wavelet-based denoising is a step towards objective Bayesian wavelet-based denoising. The result is a remarkably simple fixed non-linear shrinkage/thresholding rule which performs better than other more computationally demanding methods.
© (1999) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Mario A. T. Figueiredo and Robert D. Nowak "Bayesian wavelet-based image estimation using noninformative priors", Proc. SPIE 3816, Mathematical Modeling, Bayesian Estimation, and Inverse Problems, (25 June 1999); https://doi.org/10.1117/12.351304
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