Nonlinear total variation based noise removal algorithms
Proceedings of the eleventh annual international conference of the Center for Nonlinear Studies on Experimental mathematics : computational issues in nonlinear science: computational issues in nonlinear science
Oscillating Patterns in Image Processing and Nonlinear Evolution Equations: The Fifteenth Dean Jacqueline B. Lewis Memorial Lectures
Restoration of SAR Images Using Recovery of Discontinuities and Non-linear Optimization
EMMCVPR '97 Proceedings of the First International Workshop on Energy Minimization Methods in Computer Vision and Pattern Recognition
Geometric Level Set Methods in Imaging,Vision,and Graphics
Geometric Level Set Methods in Imaging,Vision,and Graphics
An Algorithm for Total Variation Minimization and Applications
Journal of Mathematical Imaging and Vision
Image Decomposition into a Bounded Variation Component and an Oscillating Component
Journal of Mathematical Imaging and Vision
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This paper deals with the denoising of SAR images. We draw our inspiration from the modeling of multiplicative speckle noise. By using a MAP estimator, we propose a functional whose minimizer corresponds to the denoised image we want to recover. Although the functional is not convex, we prove the existence of a minimizer. Then we study a semi-discrete version of the associated evolution problem, for which we derive existence and uniqueness results for the solution. We prove the convergence of this semi-discrete scheme. We conclude with some numerical results.