Abstract
Adaptive sparse representation has been heavily exploited in signal processing and computer vision. Recently, sparsifying transform learning received interest for its cheap computation and optimal updates in the alternating algorithms. In this work, we develop a methodology for learning a Flipping and Rotation Invariant Sparsifying Transform, dubbed FRIST, to better represent natural images that contain textures with various geometrical directions. The proposed alternating learning algorithm involves efficient optimal updates. We demonstrate empirical convergence behavior of the proposed learning algorithm. Preliminary experiments show the usefulness of FRIST for image sparse representation, segmentation, robust inpainting, and MRI reconstruction with promising performances.
| Original language | English (US) |
|---|---|
| Title of host publication | 2016 IEEE International Conference on Image Processing, ICIP 2016 - Proceedings |
| Publisher | IEEE Computer Society |
| Pages | 3857-3861 |
| Number of pages | 5 |
| ISBN (Electronic) | 9781467399616 |
| DOIs | |
| State | Published - Aug 3 2016 |
| Event | 23rd IEEE International Conference on Image Processing, ICIP 2016 - Phoenix, United States Duration: Sep 25 2016 → Sep 28 2016 |
Publication series
| Name | Proceedings - International Conference on Image Processing, ICIP |
|---|---|
| Volume | 2016-August |
| ISSN (Print) | 1522-4880 |
Other
| Other | 23rd IEEE International Conference on Image Processing, ICIP 2016 |
|---|---|
| Country | United States |
| City | Phoenix |
| Period | 9/25/16 → 9/28/16 |
Fingerprint
Keywords
- Clustering
- Inpainting
- Magnetic resonance imaging
- Sparse representation
- Sparsifying transform
ASJC Scopus subject areas
- Software
- Computer Vision and Pattern Recognition
- Signal Processing
Cite this
Learning flipping and rotation invariant sparsifying transforms. / Wen, Bihan; Ravishankar, Saiprasad; Bresler, Yoram.
2016 IEEE International Conference on Image Processing, ICIP 2016 - Proceedings. IEEE Computer Society, 2016. p. 3857-3861 7533082 (Proceedings - International Conference on Image Processing, ICIP; Vol. 2016-August).Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
}
TY - GEN
T1 - Learning flipping and rotation invariant sparsifying transforms
AU - Wen, Bihan
AU - Ravishankar, Saiprasad
AU - Bresler, Yoram
PY - 2016/8/3
Y1 - 2016/8/3
N2 - Adaptive sparse representation has been heavily exploited in signal processing and computer vision. Recently, sparsifying transform learning received interest for its cheap computation and optimal updates in the alternating algorithms. In this work, we develop a methodology for learning a Flipping and Rotation Invariant Sparsifying Transform, dubbed FRIST, to better represent natural images that contain textures with various geometrical directions. The proposed alternating learning algorithm involves efficient optimal updates. We demonstrate empirical convergence behavior of the proposed learning algorithm. Preliminary experiments show the usefulness of FRIST for image sparse representation, segmentation, robust inpainting, and MRI reconstruction with promising performances.
AB - Adaptive sparse representation has been heavily exploited in signal processing and computer vision. Recently, sparsifying transform learning received interest for its cheap computation and optimal updates in the alternating algorithms. In this work, we develop a methodology for learning a Flipping and Rotation Invariant Sparsifying Transform, dubbed FRIST, to better represent natural images that contain textures with various geometrical directions. The proposed alternating learning algorithm involves efficient optimal updates. We demonstrate empirical convergence behavior of the proposed learning algorithm. Preliminary experiments show the usefulness of FRIST for image sparse representation, segmentation, robust inpainting, and MRI reconstruction with promising performances.
KW - Clustering
KW - Inpainting
KW - Magnetic resonance imaging
KW - Sparse representation
KW - Sparsifying transform
UR - http://www.scopus.com/inward/record.url?scp=85006788279&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85006788279&partnerID=8YFLogxK
U2 - 10.1109/ICIP.2016.7533082
DO - 10.1109/ICIP.2016.7533082
M3 - Conference contribution
AN - SCOPUS:85006788279
T3 - Proceedings - International Conference on Image Processing, ICIP
SP - 3857
EP - 3861
BT - 2016 IEEE International Conference on Image Processing, ICIP 2016 - Proceedings
PB - IEEE Computer Society
ER -