Context-based denoiser that simultaneously updates probabilities for multiple contexts

摘要

A discrete, universal denoising method is applied to a noisy signal for which the source alphabet is typically large. The method exploits a priori information regarding expected characteristics of the signal. In particular, using characteristics of a continuous tone image such as continuity and small-scale symmetry allows definition of context classes containing large numbers of image contexts having similar statistical characteristics. Use of the context classes allows extraction of more reliable indications of the characteristic of a clean signal.