|Title:||Fast Regularization of Matrix-Valued Images
|Authors:||Guy Rosman, Yu Wang, Xue-Cheng Tai, Ron Kimmel and Alfred M. Bruckstein
|Abstract:||Regularization of matrix-valued data is of importance in medical imaging, motion analysis and scene understanding. In this report we describe a novel method for efficient regularization of matrix group-valued images.
Using the augmented Lagrangian framework we separate the total-variation regularization of matrix-valued images into a regularization and projection steps, both of which are fast and parallelizable.
We demonstrate the effectiveness of our method for denoising of several group-valued image types, with data in SO(n), SE(n), and SPD(n), and discuss its convergence properties.
|Copyright||The above paper is copyright by the Technion, Author(s), or others. Please contact the author(s) for more information|
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