Mathematical Programming: Series A and B
Vector Median Filters, Inf-Sup Operations, and Coupled PDE's: Theoretical Connections
Journal of Mathematical Imaging and Vision
Numerical Methods for p-Harmonic Flows and Applications to Image Processing
SIAM Journal on Numerical Analysis
Regularization of Orthonormal Vector Sets using Coupled PDE's
VLSM '01 Proceedings of the IEEE Workshop on Variational and Level Set Methods (VLSM'01)
Median and related local filters for tensor-valued images
Signal Processing
Efficient projections onto the l1-ball for learning in high dimensions
Proceedings of the 25th international conference on Machine learning
A Curvilinear Search Method for $p$-Harmonic Flows on Spheres
SIAM Journal on Imaging Sciences
The Split Bregman Method for L1-Regularized Problems
SIAM Journal on Imaging Sciences
A New Alternating Minimization Algorithm for Total Variation Image Reconstruction
SIAM Journal on Imaging Sciences
An SL(2) Invariant Shape Median
Journal of Mathematical Imaging and Vision
A Singular Value Thresholding Algorithm for Matrix Completion
SIAM Journal on Optimization
Operator Splittings, Bregman Methods and Frame Shrinkage in Image Processing
International Journal of Computer Vision
Dithering by Differences of Convex Functions
SIAM Journal on Imaging Sciences
Matrix-valued filters as convex programs
Scale-Space'05 Proceedings of the 5th international conference on Scale Space and PDE Methods in Computer Vision
Generalized multichannel image-filtering structures
IEEE Transactions on Image Processing
Vector directional filters-a new class of multichannel image processing filters
IEEE Transactions on Image Processing
Segmentation of images with separating layers by fuzzy c-means and convex optimization
Journal of Visual Communication and Image Representation
An effective dual method for multiplicative noise removal
Journal of Visual Communication and Image Representation
Homogeneous Penalizers and Constraints in Convex Image Restoration
Journal of Mathematical Imaging and Vision
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The aim of this paper is to gain more insight into vector and matrix medians and to investigate algorithms to compute them. We prove relations between vector and matrix means and medians, particularly regarding the classical structure tensor. Moreover, we examine matrix medians corresponding to different unitarily invariant matrix norms for the case of symmetric 2x2 matrices, which frequently arise in image processing. Our findings are explained and illustrated by numerical examples. To solve the corresponding minimization problems, we propose several algorithms. Existing approaches include Weiszfeld's algorithm for the computation of @?"2 vector medians and semi-definite programming, in particular, second order cone programming, which has been used for matrix median computation. In this paper, we adapt Weiszfeld's algorithm for our setting and show that also two splitting methods, namely the alternating direction method of multipliers and the parallel proximal algorithm, can be applied for generalized vector and matrix median computations. Besides, we compare the performance of these algorithms numerically and apply them within local median filters.