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Reconstruction of signals with sparse representation in optimally dilated Hermite basis

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Abstract

Compressive sensing (CS) provides a set of powerful techniques for the reconstruction of signals with a sparse representation in some particular domain, based on a reduced number of available samples (measurements). The CS application on real-life signals is directly affected by the existence of a basis or a dictionary in which the signal is sparse or highly concentrated (approximately sparse). Motivated by the fact that the time-axis scaling (dilation) factor of Hermite functions (HF) considerably affects the signal sparsity and concentration, in this paper, we propose a CS framework for the signal reconstruction based on a matching pursuit approach using an optimal dilation factor.

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Authors and Affiliations

  1. Faculty of Electrical Engineering, University of Montenegro, 81000, Podgorica, Montenegro

    Miloš Brajović, Irena Orović & Srdjan Stanković

  2. COPELABS, Universidade Lusófona de Humanidades e Tecnologias, Lisbon, Portugal

    Irena Orović & Marko Beko

  3. Instituto de Telecomunicações, Instituto Superior Técnico, Universidade de Lisboa, Lisbon, Portugal

    Marko Beko

Authors
  1. Miloš Brajović
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  2. Irena Orović
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  3. Marko Beko
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  4. Srdjan Stanković
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Correspondence to Miloš Brajović.

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Brajović, M., Orović, I., Beko, M. et al. Reconstruction of signals with sparse representation in optimally dilated Hermite basis. SIViP 17, 2789–2797 (2023). https://doi.org/10.1007/s11760-023-02496-0

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  • DOI: https://doi.org/10.1007/s11760-023-02496-0

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