Abstract
In this paper, we investigate dictionary learning (DL) from sparsely corrupted or compressed signals. We consider three cases: I) the training signals are corrupted, and the locations of the corruptions are known, II) the locations of the sparse corruptions are unknown, and III) DL from compressed measurements, as it occurs in blind compressive sensing. We develop two efficient DL algorithms that are capable of learning dictionaries from sparsely corrupted or compressed measurements. Empirical phase transitions and an in-painting example demonstrate the capabilities of our algorithms.
Original language | English (US) |
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Title of host publication | 2012 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2012 - Proceedings |
Pages | 3341-3344 |
Number of pages | 4 |
DOIs | |
State | Published - Oct 23 2012 |
Event | 2012 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2012 - Kyoto, Japan Duration: Mar 25 2012 → Mar 30 2012 |
Other
Other | 2012 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2012 |
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Country/Territory | Japan |
City | Kyoto |
Period | 3/25/12 → 3/30/12 |
Keywords
- compressive sensing
- Dictionary learning
- in-painting
- signal restoration
- sparse approximation
ASJC Scopus subject areas
- Signal Processing
- Software
- Electrical and Electronic Engineering