A Nonlinear Entropic Variational Model for Image Filtering

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.
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Article dans une revue
EURASIP Journal on Advances in Signal Processing, SpringerOpen, 2004, 2004 (16), pp.540425
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A Ben Hamza, Hamid Krim, Josiane Zerubia. A Nonlinear Entropic Variational Model for Image Filtering. EURASIP Journal on Advances in Signal Processing, SpringerOpen, 2004, 2004 (16), pp.540425. <hal-00784485>

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