Morphological operators for image sequences
Computer Vision and Image Understanding
Color image processing and applications
Color image processing and applications
Geometric partial differential equations and image analysis
Geometric partial differential equations and image analysis
Fast Approximate Energy Minimization via Graph Cuts
IEEE Transactions on Pattern Analysis and Machine Intelligence
Geometry and Color in Natural Images
Journal of Mathematical Imaging and Vision
Adaptive vector median filtering
Pattern Recognition Letters
Morphological Algorithms for Color Images based on a Generic-Programming Approach
SIBGRAPHI '98 Proceedings of the International Symposium on Computer Graphics, Image Processing, and Vision
Selection weighted vector directional filters
Computer Vision and Image Understanding - Special issue on color for image indexing and retrieval
A fast and exact algorithm for total variation minimization
IbPRIA'05 Proceedings of the Second Iberian conference on Pattern Recognition and Image Analysis - Volume Part I
Color TV: total variation methods for restoration of vector-valued images
IEEE Transactions on Image Processing
Morphological operators on the unit circle
IEEE Transactions on Image Processing
Image Restoration with Discrete Constrained Total Variation Part I: Fast and Exact Optimization
Journal of Mathematical Imaging and Vision
Global optimization for first order Markov Random Fields with submodular priors
Discrete Applied Mathematics
SAR image regularization with fast approximate discrete minimization
IEEE Transactions on Image Processing
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We present a vectorial self dual morphological filter. Contrary to many methods, our approach does not require the use of an ordering on vectors. It relies on the minimization of the total variation with L1 norm as data fidelity on each channel. We further constraint this minimization in order not to create new values. It is shown that this minimization yields a self-dual and contrast invariant filter. Although the above minimization is not a convex problem, we propose an algorithm which computes a global minimizer. This algorithm relies on minimum cost cut-based optimizations.