Fast regularization of matrix-valued images

  • Authors:
  • Guy Rosman;Yu Wang;Xue-Cheng Tai;Ron Kimmel;Alfred M. Bruckstein

  • Affiliations:
  • Dept. of Computer Science, Technion - IIT, Haifa, Israel;Dept. of Computer Science, Technion - IIT, Haifa, Israel;Dept. of Mathematics, University of Bergen, Bergen, Norway;Dept. of Computer Science, Technion - IIT, Haifa, Israel;Dept. of Computer Science, Technion - IIT, Haifa, Israel

  • Venue:
  • ECCV'12 Proceedings of the 12th European conference on Computer Vision - Volume Part III
  • Year:
  • 2012

Quantified Score

Hi-index 0.00

Visualization

Abstract

Regularization of images with matrix-valued data is important in medical imaging, motion analysis and scene understanding. We propose a novel method for fast regularization of matrix group-valued images. Using the augmented Lagrangian framework we separate total- variation regularization of matrix-valued images into a regularization and a projection steps. Both steps are computationally efficient and easily parallelizable, allowing real-time regularization of matrix valued images on a graphic processing unit. We demonstrate the effectiveness of our method for smoothing several group-valued image types, with applications in directions diffusion, motion analysis from depth sensors, and DT-MRI denoising.