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Many natural signals exhibit a sparse representation, whenever a suitable describing model is given. Here, a linear generative model is considered, where many sparsity-based signal processing techniques rely on such a simplified model. As this model is often unknown for many classes of the signals, we need to select such a model based on the domain knowledge or using some exemplar signals. This paper presents a new exemplar based approach for the linear model (called the dictionary) selection, for such sparse inverse problems. The problem of dictionary selection, which has also been called the dictionary learning in this setting, is first reformulated as a joint sparsity model. The joint sparsity model here differs from the standard joint sparsity model as it considers an overcompleteness in the representation of each signal, within the range of selected subspaces. The new dictionary selection paradigm is examined with some synthetic and realistic simulations.
|Pages (from-to)||4547 - 4556|
|Number of pages||10|
|Journal||IEEE Transactions on Signal Processing|
|Early online date||10 Jul 2014|
|Publication status||Published - 1 Sep 2014|
- sparse approximation
- Compressed sensing (CS)
- Dictionary Learning
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- 1 Finished
Signal Processing in the Networked Battlespace
Mulgrew, B., Davies, M., Hopgood, J. & Thompson, J.
1/04/13 → 30/06/18
Mehrdad Yaghoobi Vaighan
- School of Engineering - Lecturer in Signal and Image Processing
Person: Academic: Research Active