Modeling Multiscale Subbands of Photographic Images with Fields of Gaussian Scale Mixtures
The local statistical properties of photographic images, when represented in a multiscale basis, have been described using Gaussian scale mixtures. Here, we use this local description as a substrate for constructing a global field of Gaussian scale mixtures (FoGSM). Specifically, we model multiscale subbands as a product of an exponentiated homogeneous Gaussian Markov random field (hGMRF) and a second independent hGMRF. We show that parameter estimation for this model is feasible and that samples drawn from a FoGSM model have marginal and joint statistics similar to those of the subband coefficients of photographic images. We develop an algorithm for removing additive white Gaussian noise based on the FoGSM model and demonstrate denoising performance comparable with state-of-the-art methods.
markov random field image statistics image denoising