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2008

Image Modeling and Denoising With Orientation-Adapted Gaussian Scale Mixtures

9 years 10 months ago
Image Modeling and Denoising With Orientation-Adapted Gaussian Scale Mixtures
We develop a statistical model to describe the spatially varying behavior of local neighborhoods of coefficients in a multiscale image representation. Neighborhoods are modeled as samples of a multivariate Gaussian density that are modulated and rotated according to the values of two hidden random variables, thus allowing the model to adapt to the local amplitude and orientation of the signal. A third hidden variable selects between this oriented process and a nonoriented scale mixture of Gaussians process, thus providing adaptability to the local orientedness of the signal. Based on this model, we develop an optimal Bayesian least squares estimator for denoising images and show through simulations that the resulting method exhibits significant improvement over previously published results obtained with Gaussian scale mixtures.
David K. Hammond, Eero P. Simoncelli
Added 15 Dec 2010
Updated 15 Dec 2010
Type Journal
Year 2008
Where TIP
Authors David K. Hammond, Eero P. Simoncelli
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