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Matrix-Valued Filters as Convex Programs

  • Martin Welk
  • Florian Becker
  • Christoph Schnörr
  • Joachim Weickert
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3459)

Abstract

Matrix-valued images gain increasing importance both as the output of new imaging techniques and as the result of image processing operations, bearing the need for robust and efficient filters for such images. Recently, a median filter for matrix-valued images has been introduced. We propose a new approach for the numerical computation of matrix-valued median filters, and closely related mid-range filters, based on sound convex programming techniques. Matrix-valued medians are uniquely computed as global optima with interior point solvers. The robust performance is validated with experimental results for matrix-valued data including texture analysis and denoising.

Keywords

Convex Programming Structure Tensor Impulse Noise Frobenius Norm Texture Segmentation 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2005

Authors and Affiliations

  • Martin Welk
    • 1
  • Florian Becker
    • 2
  • Christoph Schnörr
    • 2
  • Joachim Weickert
    • 1
  1. 1.Mathematical Image Analysis Group, Faculty of Mathematics and Computer Science, Bldg. 27Saarland UniversitySaarbrückenGermany
  2. 2.Computer Vision, Graphics, and Pattern Recognition Group, Faculty of Mathematics and Computer ScienceUniversity of MannheimMannheimGermany

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