Open Access

A Nonlinear Entropic Variational Model for Image Filtering

EURASIP Journal on Advances in Signal Processing20042004:540425

Received: 12 August 2003

Published: 2 December 2004


We propose an information-theoretic variational filter for image denoising. It is a result of minimizing a functional subject to some noise constraints, and takes a hybrid form of a negentropy variational integral for small gradient magnitudes and a total variational integral for large gradient magnitudes. The core idea behind this approach is to use geometric insight in helping to construct regularizing functionals and avoiding a subjective choice of a prior in maximum a posteriori estimation. Illustrative experimental results demonstrate a much improved performance of the approach in the presence of Gaussian and heavy-tailed noise.

Keywords and phrases

MAP estimation variational methods robust statistics differential entropy gradient descent flows image denoising

Authors’ Affiliations

Concordia Institute for Information Systems Engineering, Concordia University
Department of Electrical and Computer Engineering, North Carolina State University
Ariana Research Group, INRIA/I3S


© Ben Hamza et al. 2004