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International Conference on Green Communications and Networking

GreeNets 2019: Green Energy and Networking pp 92–100Cite as

Dimension Selection and Compression Reconstruction Algorithm of Measurement Matrix Based on Edge Density

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Abstract

This paper is based on the sparse representation of signals in orthogonal space. Data collection and compressed are combined by compressed sensing theory. The image signal can be reconstructed by fewer observations which we obtained it under the measurement matrix. Compressive sensing theory breaks through the limitation of data sampling. In the theory of compressed sensing, the selection of measurement matrix plays a key role in whether the compressed signal can be reconstructed or not. In this paper, different measurement matrices are selected to achieve the compressive sensing and their similarity coefficient matrices are analyzed to compare the different performance. This paper focus on the coefficient selection of random measurement matrix. To find the relationship between the image structure similarity coefficient and the other characteristic indexes. An algorithm for dimension design of measurement matrix is proposed. A high performance algorithm for image compression perception and image restoration is implemented.

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Authors and Affiliations

  1. Electronic Engineering College, Heilongjiang University, Harbin, China

    Jiayin Yu & Erfu Wang

Authors
  1. Jiayin Yu
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  2. Erfu Wang
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Corresponding author

Correspondence to Erfu Wang .

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Editors and Affiliations

  1. Dalian Polytechnic University, Dalian, China

    Jiyu Jin

  2. Dalian Polytechnic University, Dalian, China

    Peng Li

  3. Dalian Polytechnic University, Dalian, China

    Lei Fan

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© 2019 ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering

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Yu, J., Wang, E. (2019). Dimension Selection and Compression Reconstruction Algorithm of Measurement Matrix Based on Edge Density. In: Jin, J., Li, P., Fan, L. (eds) Green Energy and Networking. GreeNets 2019. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 282. Springer, Cham. https://doi.org/10.1007/978-3-030-21730-3_10

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  • DOI: https://doi.org/10.1007/978-3-030-21730-3_10

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-21729-7

  • Online ISBN: 978-3-030-21730-3

  • eBook Packages: Computer ScienceComputer Science (R0)

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