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  • 1.
    Leffler, Klara
    et al.
    Umeå University, Faculty of Science and Technology, Department of Mathematics and Mathematical Statistics.
    Axelsson, Jan
    Umeå University, Faculty of Medicine, Department of Radiation Sciences.
    Larsson, Anne
    Umeå University, Faculty of Medicine, Department of Radiation Sciences.
    Häggström, Ida
    Umeå University, Faculty of Medicine, Department of Radiation Sciences.
    Yu, Jun
    Umeå University, Faculty of Science and Technology, Department of Mathematics and Mathematical Statistics.
    Sharper Positron Emission Tomography: Intelligent Data Sampling to Promote Accelerated Medical Imaging2019Conference paper (Other academic)
  • 2.
    Leffler, Klara
    et al.
    Umeå University, Faculty of Science and Technology, Department of Mathematics and Mathematical Statistics.
    Häggström, Ida
    Umeå University, Faculty of Medicine, Department of Radiation Sciences, Radiation Physics.
    Yu, Jun
    Umeå University, Faculty of Science and Technology, Department of Mathematics and Mathematical Statistics.
    Intelligent data sampling promotes accelerated medical imaging: sharper positron emission tomography2018Conference paper (Refereed)
  • 3.
    Leffler, Klara
    et al.
    Umeå University, Faculty of Science and Technology, Department of Mathematics and Mathematical Statistics.
    Zhou, Zhiyong
    Umeå University, Faculty of Science and Technology, Department of Mathematics and Mathematical Statistics.
    Yu, Jun
    Umeå University, Faculty of Science and Technology, Department of Mathematics and Mathematical Statistics.
    An Extended Block Restricted Isometry Property for Sparse Recovery with Non-Gaussian Noise2018Conference paper (Refereed)
  • 4.
    Leffler, Klara
    et al.
    Umeå University, Faculty of Science and Technology, Department of Mathematics and Mathematical Statistics.
    Zhou, Zhiyong
    Department of Statistics, Zhejiang University City College, China.
    Yu, Jun
    Umeå University, Faculty of Science and Technology, Department of Mathematics and Mathematical Statistics.
    An extended block restricted isometry property for sparse recovery with non-Gaussian noise2019In: Journal of Computational Mathematics, ISSN 0254-9409, E-ISSN 1991-7139Article in journal (Refereed)
    Abstract [en]

    We study the recovery conditions of weighted mixed ℓ2/ℓp minimization for block sparse signal reconstruction from compressed measurements when partial block supportinformation is available. We show theoretically that the extended block restricted isometry property can ensure robust recovery when the data fidelity constraint is expressed in terms of an ℓq norm of the residual error, thus establishing a setting wherein we arenot restricted to Gaussian measurement noise. We illustrate the results with a series of numerical experiments.

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