Fast regularization of matrix-valued images
Publication
, Conference
Rosman, G; Wang, Y; Tai, XC; Kimmel, R; Bruckstein, AM
Published in: Lecture Notes in Computer Science Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics
October 30, 2012
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.
Duke Scholars
Published In
Lecture Notes in Computer Science Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics
DOI
EISSN
1611-3349
ISSN
0302-9743
Publication Date
October 30, 2012
Volume
7574 LNCS
Issue
PART 3
Start / End Page
173 / 186
Related Subject Headings
- Artificial Intelligence & Image Processing
- 46 Information and computing sciences
Citation
APA
Chicago
ICMJE
MLA
NLM
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
Rosman, G., Y. Wang, X. C. Tai, R. Kimmel, and A. M. Bruckstein. “Fast regularization of matrix-valued images.” In Lecture Notes in Computer Science Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics, 7574 LNCS:173–86, 2012. https://doi.org/10.1007/978-3-642-33712-3_13.
Rosman G, Wang Y, Tai XC, Kimmel R, Bruckstein AM. Fast regularization of matrix-valued images. In: Lecture Notes in Computer Science Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics. 2012. p. 173–86.
Rosman, G., et al. “Fast regularization of matrix-valued images.” Lecture Notes in Computer Science Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics, vol. 7574 LNCS, no. PART 3, 2012, pp. 173–86. Scopus, doi:10.1007/978-3-642-33712-3_13.
Rosman G, Wang Y, Tai XC, Kimmel R, Bruckstein AM. Fast regularization of matrix-valued images. Lecture Notes in Computer Science Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics. 2012. p. 173–186.
Published In
Lecture Notes in Computer Science Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics
DOI
EISSN
1611-3349
ISSN
0302-9743
Publication Date
October 30, 2012
Volume
7574 LNCS
Issue
PART 3
Start / End Page
173 / 186
Related Subject Headings
- Artificial Intelligence & Image Processing
- 46 Information and computing sciences