Modeling Multiscale Subbands of Photographic Images with Fields of Gaussian Scale Mixtures

Authors:
Siwei Lyu;Eero P. Simoncelli
Affiliations:
University at Albany, State University of New York, Albany;Howard Hughes Medical Institute and New York University, New York
Venue:
IEEE Transactions on Pattern Analysis and Machine Intelligence
Year:
2009

Citing 0
Cited 6

Nonlinear extraction of independent components of natural images using radial gaussianization

Neural Computation
Image denoising using mixtures of projected Gaussian scale mixtures

IEEE Transactions on Image Processing
Is denoising dead?

IEEE Transactions on Image Processing
A Bayesian framework for image segmentation with spatially varying mixtures

IEEE Transactions on Image Processing
Dependency reduction with divisive normalization: Justification and effectiveness

Neural Computation
Optical coherence tomography noise reduction using anisotropic local bivariate gaussian mixture prior in 3D complex wavelet domain

Journal of Biomedical Imaging

Quantified Score

Hi-index	0.14

Visualization

Abstract

The local statistical properties of photographic images, when represented in a multi-scale 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 (FoGSMs). Specifically, we model multi-scale 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 subband coefficients of photographic images. We develop an algorithm for removing additive Gaussian white noise based on the FoGSM model, and demonstrate denoising performance comparable with state-of-the-art methods.