- Conference Paper
Rights / licenseIn Copyright - Non-Commercial Use Permitted
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. Show more
Book title2012 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
Pages / Article No.
SubjectDictionary learning; Sparse approximation; Compressive sensing; Signal restoration; In-painting
Organisational unit09695 - Studer, Christoph / Studer, Christoph
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