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Deterministic Bipolar Compressed Sensing Matrices from Binary Sequence Family

Lu, Cunbo ; Chen, Wengu ; Xu, Haibo

KSII Transactions on Internet and Information Systems, 2020, 14(6), , pp.2497-2517 [Periódico revisado por pares]

한국인터넷정보학회

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  • Título:
    Deterministic Bipolar Compressed Sensing Matrices from Binary Sequence Family
  • Autor: Lu, Cunbo ; Chen, Wengu ; Xu, Haibo
  • Assuntos: binary sequence family ; coherence ; Compressed sensing ; measurement matrix ; 컴퓨터학
  • É parte de: KSII Transactions on Internet and Information Systems, 2020, 14(6), , pp.2497-2517
  • Notas: Korean Society for Internet Information
    KISTI1.1003/JNL.JAKO202022762159623
  • Descrição: For compressed sensing (CS) applications, it is significant to construct deterministic measurement matrices with good practical features, including good sensing performance, low memory cost, low computational complexity and easy hardware implementation. In this paper, a deterministic construction method of bipolar measurement matrices is presented based on binary sequence family (BSF). This method is of interest to be applied for sparse signal restore and image block CS. Coherence is an important tool to describe and compare the performance of various sensing matrices. Lower coherence implies higher reconstruction accuracy. The coherence of proposed measurement matrices is analyzed and derived to be smaller than the corresponding Gaussian and Bernoulli random matrices. Simulation experiments show that the proposed matrices outperform the corresponding Gaussian, Bernoulli, binary and chaotic bipolar matrices in reconstruction accuracy. Meanwhile, the proposed matrices can reduce the reconstruction time compared with their Gaussian counterpart. Moreover, the proposed matrices are very efficient for sensing performance, memory, complexity and hardware realization, which is beneficial to practical CS.
  • Editor: 한국인터넷정보학회
  • Idioma: Coreano;Inglês
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