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- W2106793721 abstract "Assuming that a set of source signals is sparsely representable in a given dictionary, we show how their sparse recovery fails whenever we can only measure a convolved observation of them. Starting from this motivation, we develop a block coordinate descent method which aims to learn a convolved dictionary and provide a sparse representation of the observed signals with small residual norm. We compare the proposed approach to the K-SVD dictionary learning algorithm and show through numerical experiment on synthetic signals that, provided some conditions on the problem data, our technique converges in a fixed number of iterations to a sparse representation with smaller residual norm." @default.
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- W2106793721 date "2011-05-01" @default.
- W2106793721 modified "2023-09-25" @default.
- W2106793721 title "Dictionary learning of convolved signals" @default.
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- W2106793721 doi "https://doi.org/10.1109/icassp.2011.5947682" @default.
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