Perceptual Quality Improvement for Synthesis Imaging of Chinese Spectral Radioheliograph

  • Long XuEmail author
  • Lin Ma
  • Zhuo Chen
  • Yihua Yan
  • Jinjian Wu
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9315)


Chinese Spectral Radioheliography can generate the images of the Sun with good spatial resolutions. It employs the Aperture Synthesis principle to image the Sun with plentiful solar radio activities. However, due to the limitation of the hardware, specifically the limited number of antennas, the recorded signal is extremely sparse in practice, which results in unsatisfied solar radio image quality. In this paper, we study the image reconstruction of Chinese Spectral RadioHeliograph (CSRH) by the aid of compressed sensing (CS) technique. In our proposed method, we adopt dictionary technique to represent solar radio images sparsely. The experimental results indicate that the proposed algorithm contributes both PSNR and subjective image quality improvements of synthesis imaging of CSRH markedly.


Compressed sensing Solar radio astronomy Image reconstruction Aperture syntheis 



This work was partially supported by a grant from the National Natural Science Foundation of China under Grant 61202242, 100-Talents Program of Chinese Academy of Sciences (No. Y434061V01).


  1. 1.
    Hőgbom, J.A.: Aperture synthesis with a non-regular distribution of interferometer baselines. Astron. Astrophys. Suppl. 15, 417 (1974) Google Scholar
  2. 2.
    Thompson, A.R., Moran, J.M., Swenson, G.W., Wakker, B.P., Schwarz, U.J.: The Multi-Resolution CLEAN and its application to the short-spacing problem in interferometry. Astron. Astrophys. 200, 312–322 (1988) Google Scholar
  3. 3.
    Cornwell, T.J.: Multi-Scale Clean Deconvolution of Radio Synthesis Images. arXiv: 0806.2228Google Scholar
  4. 4.
    Weir, N.: A multi-channel method of maximum entropy image restoration. In: ASP Conference Series 25: Astronomical Data Analysis Software and Systems I, p. 186 (1992)Google Scholar
  5. 5.
    Cornwell, T.J., Evans, K.F.: A simple maximum entropy deconvolution algorithm. Astron. Astrophys. 143, 77–83 (1985) Google Scholar
  6. 6.
    Starck, J.L., Pantin, E., Murtagh, F.: Deconvolution in astronomy: a review. Publ. Astr. Soc. Pacific 114, 1051–1069 (2002) CrossRefGoogle Scholar
  7. 7.
    Cand`es, E., Romberg, J., Tao, T.: Robust uncertainty principles: exact signal reconstruction from highly incomplete frequency information. IEEE Trans. Inform. Theory 52, 489–509 (2006) MathSciNetCrossRefzbMATHGoogle Scholar
  8. 8.
    Donoho, D.L.: Compressed sensing. IEEE Trans. Inf. Theory 52(4), 1289–1306 (2006) MathSciNetCrossRefzbMATHGoogle Scholar
  9. 9.
    Donoho, D.L., Huo, X.: Uncertainty principles and ideal atomic decompositions. IEEE Trans. Inform. Theory 47, 2845–2862 (2011) MathSciNetCrossRefzbMATHGoogle Scholar
  10. 10.
    Elad, M., Aharon, M.: Image denoising via sparse and redundant representations over learned dictionaries. IEEE Trans. Image Process. 15(12), 3736–3745 (2006) MathSciNetCrossRefGoogle Scholar
  11. 11.
    Buades, A., Coll, B., Morel, J.M.: A review of image denoising algorithms, with a new one. Multiscale Model. Simul. 4(2), 490–530 (2005) MathSciNetCrossRefzbMATHGoogle Scholar
  12. 12.
    Yang, J., Wright, J., Huang, T.S., et al.: Image super-resolution via sparse representation. IEEE Trans. Image Process. 19(11), 2861–2873 (2010) MathSciNetCrossRefGoogle Scholar
  13. 13.
    Zhang, J., Zhao, D.B., Gao, W.: Group-based sparse representation for image restoration. IEEE Trans. Image Process. (TIP) 23(8), 3336–3351 (2014) MathSciNetCrossRefGoogle Scholar
  14. 14.
    Zhang, J., Zhao, D.B., Xiong, R.Q., Ma, S.W., Gao, W.: Image restoration using joint statistical modeling in a space-transform domain. IEEE Trans. Circuits Syst. Video Technol. (TCSVT) 24(6), 915–928 (2014) CrossRefGoogle Scholar
  15. 15.
    Yan, Y., Wang, W., Liu,F., Geng, L., Zhang, J.: Radio imaging-spectroscopy observation of the sun in decimeteric and centimetric wavelengths. In: Solar and Astrophysical Dynamos and Magnetic Activity, Proceeding of IAU Symposium, no. 294, pp. 489–494 (2012)Google Scholar
  16. 16.
    Yan, Y., Zhang, J., Wang, W., Liu, F., Chen, Z., Ji, G.: The Chinese spectral Radioheliograph-CSRH. Earth Moon Planet. 104, 97–100 (2009) CrossRefGoogle Scholar
  17. 17.
    Du, J., Yan, Y.H., Wang, W.: A simulation of imaging capabilities for the Chinese Spectral Radioheliograph. In: IAU Symposium, pp. 501–502 (2013)Google Scholar
  18. 18.
  19. 19.
    Li, F., Cornwell, T., de Hoog, F.: The application of compressive sampling to radio astronomy I: deconvolution. Astron. Astrophys. 528(A31), 1–10 (2011) Google Scholar
  20. 20.
    Li, F., Brown, S., Cornwell, T., de Hoog, F.: The application of compressive sampling to radio astronomy II: faraday rotation measure synthesis. Accepted Astron. Astrophys. 531, A126 (2011) CrossRefGoogle Scholar
  21. 21.
    Wenger, S., Magnor, M., Pihlström, Y., et al.: SparseRI: A compressed sensing framework for aperture synthesis imaging in radio astronomy. Publ. Astron. Soc. Pac. 122(897), 1367–1374 (2010) CrossRefGoogle Scholar
  22. 22.
    Bobin, J., Starck, J.L., Ottensamer, R.: Compressed sensing in astronomy. IEEE J. Sel. Top. Sign. Process. 2(5), 718–726 (2008) CrossRefGoogle Scholar
  23. 23.
    Wiaux, Y., Jacques, L., Puy, G., et al.: Compressed sensing imaging techniques for radio interferometry. Mon. Not. Roy. Astron. Soc. 395(3), 1733–1742 (2009) CrossRefGoogle Scholar
  24. 24.
    Wang, Z., Bovik, A.C., Sheikh, H.R., Simoncelli, E.P.: Image quality assessment: from error visibility to structural similarity. IEEE Trans. Image Process. 13(4), 600–612 (2004) CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Long Xu
    • 1
    Email author
  • Lin Ma
    • 2
  • Zhuo Chen
    • 1
  • Yihua Yan
    • 1
  • Jinjian Wu
    • 3
  1. 1.Key Laboratory of Solar ActivityNational Astronomical Observatories, Chinese Academy of SciencesBeijingChina
  2. 2.Huawei Noah’s Ark LabHong KongChina
  3. 3.School of Electronic EngineeringXidian UniversityXi’anChina

Personalised recommendations