YANG Yi, DENG Li, DUAN Ran, et al. A image reconstruction algorithm of transient sources based on combined sparsities of background and variation[J]. Journal of Beijing University of Aeronautics and Astronautics, 2020, 46(5): 915-924. doi: 10.13700/j.bh.1001-5965.2019.0222(in Chinese)
Citation: YANG Yi, DENG Li, DUAN Ran, et al. A image reconstruction algorithm of transient sources based on combined sparsities of background and variation[J]. Journal of Beijing University of Aeronautics and Astronautics, 2020, 46(5): 915-924. doi: 10.13700/j.bh.1001-5965.2019.0222(in Chinese)

A image reconstruction algorithm of transient sources based on combined sparsities of background and variation

doi: 10.13700/j.bh.1001-5965.2019.0222
Funds:

Key Research Program of Frontier Sciences, CAS QYZDY-SSW-JSC014

More Information
  • Corresponding author: DENG Li, E-mail: dengli@nssc.ac.cn
  • Received Date: 12 May 2019
  • Accepted Date: 06 Dec 2019
  • Publish Date: 20 May 2020
  • Radio interferometers can achieve high spatial resolution imaging by combining multiple groups of visibility data measured over long periods of time. However, the variable information of temporally variable source is missing. A image reconstruction algorithm of varied sources by sparse baseline aperture synthesis based on sparse constraint on direct sum of background and inter-frame difference is proposed. The brightness temperature at initial moment and the brightness temperature difference of adjacent moments are taken as the vector of solution to seek, and the brightness temperatures at different moments are the sums of them, which leads to the measuring equation of the brightness temperature at initial moment and the difference. Transient source images at different moments are reconstructed by solving the sparsity of brightness temperature at initial moment and brightness temperature difference of adjacent moments. The results of numerical experiments show that the proposed method matches the best on transient source in a local background and outperforms the existing methods on varying source in a global background.

     

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