A nonlinear entropic variational model for image filtering

Authors:
A. Ben Hamza;Hamid Krim;Josiane Zerubia
Affiliations:
Concordia Institute for Information Systems Engineering, Concordia University, Montréal, Quebec, Canada;Department of Electrical and Computer Engineering, North Carolina State University, Raleigh, NC;Ariana Research Group, INRIA/I3S, Sophia Antipolis Cedex, France
Venue:
EURASIP Journal on Applied Signal Processing
Year:
2004

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Abstract

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.