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- W1571990599 abstract "In this paper, we develop a multivariate statistical approach for image denoising in the wavelet transformed domain. To this respect, the wavelet coefficients of all the image channels at the same spatial position, in a given orientation and at the same resolution level, are grouped into a vector and a maximum a posteriori estimate is derived from a multivariate Bernouilli-Gaussian prior. The parameters of this statistical model are computed recursively from coarse to fine resolutions in order to exploit the inter-scale redundancies between the wavelet coefficients. Simulation tests performed on remote sensing multispectral images indicate that the proposed procedure improves the conventional wavelet-based denoising methods." @default.
- W1571990599 created "2016-06-24" @default.
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- W1571990599 date "2004-09-01" @default.
- W1571990599 modified "2023-10-15" @default.
- W1571990599 title "An interscale multivariate MAP estimation of multispectral images" @default.
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