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  • Research Article
  • Open Access

A Nonlinear Entropic Variational Model for Image Filtering

EURASIP Journal on Advances in Signal Processing20042004:540425

  • Received: 12 August 2003
  • Published:


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, Montréal, Quebec, H3G 1T7, Canada
Department of Electrical and Computer Engineering, North Carolina State University, Raleigh, NC 27695-7911, USA
Ariana Research Group, INRIA/I3S, BP 93, Sophia Antipolis Cedex, 06902, France


© Ben Hamza et al. 2004