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. © 2012 Springer-Verlag.
CITATION STYLE
Rosman, G., Wang, Y., Tai, X. C., Kimmel, R., & Bruckstein, A. M. (2012). Fast regularization of matrix-valued images. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 7574 LNCS, pp. 173–186). https://doi.org/10.1007/978-3-642-33712-3_13
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