• DocumentCode
    3339152
  • Title

    The simplest measurement matrix for compressed sensing of natural images

  • Author

    He, Zaixing ; Ogawa, Takahiro ; Haseyama, Miki

  • Author_Institution
    Grad. Sch. of Inf. Sci. & Technol., Hokkaido Univ., Sapporo, Japan
  • fYear
    2010
  • fDate
    26-29 Sept. 2010
  • Firstpage
    4301
  • Lastpage
    4304
  • Abstract
    There exist two main problems in currently existing measurement matrices for compressed sensing of natural images, the difficulty of hardware implementation and low sensing efficiency. In this paper, we present a novel simple and efficient measurement matrix, Binary Permuted Block Diagonal (BPBD) matrix. The BPBD matrix is binary and highly sparse (all but one or several “1”s in each column are “0”s). Therefore, it can simplify the compressed sensing procedure dramatically. The proposed measurement matrix has the following advantages, which cannot be entirely satisfied by existing measurement matrices. (1) It has easy hardware implementation because of the binary elements; (2) It has high sensing efficiency because of the highly sparse structure; (3) It is incoherent with different popular sparsity basis´ like wavelet basis and gradient basis; (4) It provides fast and nearly optimal reconstructions. Moreover, the simulation results demonstrate the advantages of the proposed measurement matrix.
  • Keywords
    image coding; matrix algebra; natural scenes; BPBD matrix; binary elements; binary permuted block diagonal matrix; compressed sensing procedure; gradient basis; hardware implementation; high sensing efficiency; highly sparse structure; low sensing efficiency; measurement matrices; natural images; optimal reconstructions; popular sparsity basis; simplest measurement matrix; wavelet basis; Coherence; Compressed sensing; Hardware; Image reconstruction; Minimization; Sensors; Sparse matrices; Compressed sensing; binary permuted block diagonal matrix; hardware implementation; sensing efficiency;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Image Processing (ICIP), 2010 17th IEEE International Conference on
  • Conference_Location
    Hong Kong
  • ISSN
    1522-4880
  • Print_ISBN
    978-1-4244-7992-4
  • Electronic_ISBN
    1522-4880
  • Type

    conf

  • DOI
    10.1109/ICIP.2010.5651800
  • Filename
    5651800