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An Approach to 2D Signals Recovering in Compressive Sensing Context


In this paper, we study the compressive sensing effects on 2D signals exhibiting sparsity in 2D DFT domain. A simple algorithm for reconstruction of randomly undersampled data is proposed. It is based on the analytically determined threshold that precisely separates signal and non-signal components in the 2D DFT domain. The algorithm operates fast in a single iteration providing the accurate signal reconstruction. In the situations that are not comprised by the analytic derivation and constrains, the algorithm is still efficient and need just a couple of iterations. The proposed solution shows promising results in ISAR imaging (simulated data are used), where the reconstruction is achieved even in the case when less than 10 % of data are available.

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This work is supported by the Montenegrin Ministry of Science, project grant funded by the World Bank loan: CS-ICT “New ICT Compressive sensing based trends applied to: multimedia, biomedicine and communications.”

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Correspondence to Irena Orović.

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Stanković, S., Orović, I. An Approach to 2D Signals Recovering in Compressive Sensing Context. Circuits Syst Signal Process 36, 1700–1713 (2017).

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  • Compressive sensing
  • Signal reconstruction
  • Missing data
  • ISAR imaging
  • Sparsity