1687-6180-2004-540425 1687-6180 Research Article <p>A Nonlinear Entropic Variational Model for Image Filtering</p> Ben HamzaAhamza@ciise.concordia.ca KrimHamidahk@ncsu.edu ZerubiaJosianejosiane.zerubia@inria.fr

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

EURASIP Journal on Advances in Signal Processing 1687-6180 2004 2004 16 540425 http://asp.eurasipjournals.com/content/2004/16/540425 10.1155/S1110865704407197
12820038620042122004 2004Ben Hamza et al. MAP estimation variational methods robust statistics differential entropy gradient descent flows image denoising

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.

Nonlinear Signal and Image Processing - Part II