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.
This research was supported by Israel Science Foundation grant no.1551/09 and by the European Community’s FP7- ERC program, grant agreement no. 267414.
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Rosman, G., Wang, Y., Tai, XC., Kimmel, R., Bruckstein, A.M. (2012). Fast Regularization of Matrix-Valued Images. In: Fitzgibbon, A., Lazebnik, S., Perona, P., Sato, Y., Schmid, C. (eds) Computer Vision – ECCV 2012. ECCV 2012. Lecture Notes in Computer Science, vol 7574. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-33712-3_13
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