@inproceedings{633c7cfafa164462b0f8efe477828172,
title = "Fast regularization of matrix-valued images",
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.",
keywords = "DT-MRI, Lie-groups, Matrix-valued, Motion understanding, Optimization, Regularization, Total-variation",
author = "Guy Rosman and Yu Wang and Tai, {Xue Cheng} and Ron Kimmel and Bruckstein, {Alfred M.}",
note = "Funding Information: This research was supported by Israel Science Foundation grant no.1551/09 and by the European Community{\textquoteright}s FP7- ERC program, grant agreement no. 267414.; 12th European Conference on Computer Vision, ECCV 2012 ; Conference date: 07-10-2012 Through 13-10-2012",
year = "2012",
doi = "10.1007/978-3-642-33712-3_13",
language = "الإنجليزيّة",
isbn = "9783642337116",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
number = "PART 3",
pages = "173--186",
booktitle = "Computer Vision, ECCV 2012 - 12th European Conference on Computer Vision, Proceedings",
edition = "PART 3",
}