Dictionary learning from sparsely corrupted or compressed signals
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Date
2012
Publication Type
Conference Paper
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no
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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.
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published
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Book title
2012 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
Journal / series
Volume
Pages / Article No.
3341 - 3344
Publisher
IEEE
Event
37th International Conference on Acoustics, Speech, and Signal Processing (ICASSP 2012)
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Methods
Software
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Date collected
Date created
Subject
Dictionary learning; Sparse approximation; Compressive sensing; Signal restoration; In-painting
Organisational unit
09695 - Studer, Christoph / Studer, Christoph