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Image reconstruction from sparse projections using S-transform

Journal Article


Abstract


  • Sparse projections are an effective way to reduce

    the exposure to radiation during X-ray CT imaging. However,

    reconstruction of images from sparse projection data

    is challenging. This paper introduces a new sparse transform,

    referred to as S-transform, and proposes an accurate

    image reconstruction method based on the transform. The

    S-transform effectively converts the ill-posed reconstruction

    problem into a well-defined one by representing the image

    using a small set of transform coefficients. An algorithm is

    proposed that efficiently estimates the S-transform coefficients

    from the sparse projections, thus allowing the image

    to be accurately reconstructed using the inverse S-transform.

    The experimental results on both simulated and real images

    have consistently shown that, compared to the popular total

    variation (TV) method, the proposed method achieves comparable

    results when the projections is sparse, and substantially

    improves the quality of the reconstructed image when

    the number of the projections is relatively high. Therefore,

    the use of the proposed reconstruction algorithm may permit

    reduction of the radiation exposure without trade-off in

    imaging performance.

UOW Authors


  •   Luo, Jianhua (external author)
  •   Liu, Jiahai (external author)
  •   Li, Wanqing
  •   Zhu, Yuemin (external author)
  •   Jiang, Ruiyao (external author)

Publication Date


  • 2012

Citation


  • Luo, J., Liu, J., Li, W., Zhu, Y. & Jiang, R. (2012). Image reconstruction from sparse projections using S-transform. Journal of Mathematical Imaging and Vision, 43 (3), 227-239.

Scopus Eid


  • 2-s2.0-84859420688

Ro Metadata Url


  • http://ro.uow.edu.au/infopapers/2026

Number Of Pages


  • 12

Start Page


  • 227

End Page


  • 239

Volume


  • 43

Issue


  • 3

Abstract


  • Sparse projections are an effective way to reduce

    the exposure to radiation during X-ray CT imaging. However,

    reconstruction of images from sparse projection data

    is challenging. This paper introduces a new sparse transform,

    referred to as S-transform, and proposes an accurate

    image reconstruction method based on the transform. The

    S-transform effectively converts the ill-posed reconstruction

    problem into a well-defined one by representing the image

    using a small set of transform coefficients. An algorithm is

    proposed that efficiently estimates the S-transform coefficients

    from the sparse projections, thus allowing the image

    to be accurately reconstructed using the inverse S-transform.

    The experimental results on both simulated and real images

    have consistently shown that, compared to the popular total

    variation (TV) method, the proposed method achieves comparable

    results when the projections is sparse, and substantially

    improves the quality of the reconstructed image when

    the number of the projections is relatively high. Therefore,

    the use of the proposed reconstruction algorithm may permit

    reduction of the radiation exposure without trade-off in

    imaging performance.

UOW Authors


  •   Luo, Jianhua (external author)
  •   Liu, Jiahai (external author)
  •   Li, Wanqing
  •   Zhu, Yuemin (external author)
  •   Jiang, Ruiyao (external author)

Publication Date


  • 2012

Citation


  • Luo, J., Liu, J., Li, W., Zhu, Y. & Jiang, R. (2012). Image reconstruction from sparse projections using S-transform. Journal of Mathematical Imaging and Vision, 43 (3), 227-239.

Scopus Eid


  • 2-s2.0-84859420688

Ro Metadata Url


  • http://ro.uow.edu.au/infopapers/2026

Number Of Pages


  • 12

Start Page


  • 227

End Page


  • 239

Volume


  • 43

Issue


  • 3