期刊
IEEE TRANSACTIONS ON IMAGE PROCESSING
卷 17, 期 11, 页码 2089-2101出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TIP.2008.2004796
关键词
Gaussian Scale Mixtures; image denoising; image processing; statistical image modeling; wavelet transforms
资金
- Howard Hughes Medical Institute Funding Source: Medline
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.
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