Gaussian Scale Mixtures (GSMs) in overcomplete oriented pyramids are, arguably, one of the most powerful available tools for image denoising: 1) they provide a new mathematical frame for modelling the variance-adaptation problem, an approach used in image denoising for the last 25 years; 2) they are applicable to contaminating sources of any spectral density; 3) they yield the smallest L2-norm distortion results in simulations under white Gaussian noise, up to this date; and 4) they allow for a solution, for the first time, to the problem of denoising images affected by unknown covariance noise. In this work, we focus first on the general properties of the GSMs. Then, we review the different ways GSMs have been used in overcomplete oriented pyramids (MAP-z-GSM, BLS-GSM, spatially variant GSM), and their applications: classical denoising, signal-dependent noise removal, unknown covariance noise removal and deblurring.

Date Published: 17 September 2005
PDF: 15 pages
Proc. SPIE 5914, Wavelets XI, 59141F (17 September 2005); doi: 10.1117/12.615847

Published in SPIE Proceedings Vol. 5914:
Wavelets XI
Manos Papadakis; Andrew F. Laine; Michael A. Unser, Editor(s)