DOI QR코드

DOI QR Code

DETECTION AND RESTORATION OF NON-RADIAL VARIATION OVER FULL-DISK SOLAR IMAGES

  • Yang, Yunfei (Yunnan Key Laboratory of Computer Technology Application, Faculty of Information Engineering and Automation, Kunming University of Science and Technology) ;
  • Lin, Jiaben (Key Laboratory of Solar Activity, National Astronomical Observatories, Chinese Academy of Sciences) ;
  • Feng, Song (Yunnan Key Laboratory of Computer Technology Application, Faculty of Information Engineering and Automation, Kunming University of Science and Technology) ;
  • Deng, Hui (Yunnan Key Laboratory of Computer Technology Application, Faculty of Information Engineering and Automation, Kunming University of Science and Technology) ;
  • Wang, Feng (Yunnan Key Laboratory of Computer Technology Application, Faculty of Information Engineering and Automation, Kunming University of Science and Technology) ;
  • Ji, Kaifan (Yunnan Key Laboratory of Computer Technology Application, Faculty of Information Engineering and Automation, Kunming University of Science and Technology)
  • Received : 2013.07.01
  • Accepted : 2013.09.25
  • Published : 2013.10.31

Abstract

Full-disk solar images are provided by many solar telescopes around the world. However, the observed images show Non-Radial Variation (NRV) over the disk. In this paper, we propose algorithms for detecting distortions and restoring these images. For detecting NRV, the cross-correlation coefficients matrix of radial profiles is calculated and the minimum value in the matrix is defined as the Index of Non-radial Variation (INV). This index has been utilized to evaluate the H images of GONG, and systemic variations of different instruments are obtained. For obtaining the NRV's image, a Multi-level Morphological Filter (MMF) is designed to eliminate structures produced by solar activities over the solar surface. Comparing with the median filter, the proposed filter is a better choice. The experimental results show that the effect of our automatic detection and restoration methods is significant for getting a flat and high contrast full-disk image. For investigating the effect of our method on solar features, structural similarity (SSIM) index is utilized. The high SSIM indices (close to 1) of solar features show that the details of the structures remain after NRV restoring.

Keywords

References

  1. Benkhalil, A., Zharkova, V., Ipson, S., & Zharkov, S. 2003, Automatic Identification of Active Regions (Plages) in the Full-Disk Solar Images Using Local Thresholding and Region Growing Techniques, Proceedings of the Aisb, 66, 73
  2. Bornmann, P., Winkelman, D., & Kohl, T. 1996, Automated Solar Image Processing for Flare Forecasting, Proceedings of the Solar Terrestrial Predictions Workshop, 23, 27
  3. Denker, C., Johannesson, A., Marquette, W., Goode, P., Wang, H., & Zirin, h. 1999, Synoptic $h{\alpha}$ Full-Disk Observations of the Sun from Big Bear Solar Observatory-i. Instrumentation, Image Processing, Data Products, and First Results, Solar physics, 184, 87 https://doi.org/10.1023/A:1005047906097
  4. Fuller, N., & Aboudarham, J. 2004, Automatic Detection of Solar Filaments Versus Manual Digitization, Knowledge-Based Intelligent Information and Engineering Systems, 467 (Berlin: Springer)
  5. Fuller, N., Aboudarham, J., & Bentley, R. 2005, Filament Recognition and Image Cleaning on Meudon $h{\alpha}$ Spectroheliograms, Solar physics, 227, 61 https://doi.org/10.1007/s11207-005-8364-1
  6. Gonzalez, R. C., Woods, R. E., & Eddins, S. L. 2009, Digital Image Processing Using Matlab (Tennessee: Gatesmark Publishing)
  7. Harvey, J., Bolding, J., Clark, R., Hauth, D., Hill, F., Kroll, R., Luis, G., Mills, N., Purdy, T., & Henney, C. 2011, Full-Disk Solar $h{\alpha}$ Images from Gong, Bulletin of the American Astronomical Society, 1745
  8. Kuhn, J., Lin, H., & Loranz, D. 1991, Gain Calibrating Nonuniform Image-Array Data Using Only the Image Data, PASP, 1097
  9. Loza, A., Mihaylova, L., Canagarajah, C., & Bull, D. 2006, Structural Similarity-Based Object Tracking in Video Sequences, Information Fusion, 2006 9th International Conference on, 2006. IEEE, 1
  10. Loza, A., Dixon, T., Canga, E. F., Nikolov, S., Bull, D., Canagarajah, C., Noyes, J., & Troscianko, T. 2007, Methods for Fused Image Analysis and Assessment, Advances and Challenges in Multisensor Data and Information Processing, 8, 252
  11. Maragos, P., & Schafer, R. 1987a, Morphological Filters-Part I: Their Set-Theoretic Analysis and Relations to Linear Shift-Invariant Filters, Acoustics, Speech and Signal Processing, IEEE Transactions on, 35, 1153 https://doi.org/10.1109/TASSP.1987.1165259
  12. Maragos, P., & Schafer, R. 1987b, Morphological Filters-Part II: Their Relations to Median, Order-Statistic, and Stack Filters. Acoustics, Speech and Signal Processing, IEEE Transactions on, 35, 1170 https://doi.org/10.1109/TASSP.1987.1165254
  13. Matheron, G. 1975, Random Sets and Integral Geometry (New York: Wiley)
  14. Parvati, K., Rao, P., & Mariya Das, M. 2009, Image Segmentation Using Gray-ScaleMorphology and Marker-Controlled Watershed Transformation, Discrete Dynamics in Nature and Society, 2008
  15. Pitas, I., & Venetsanopoulos, A. N. 1992, Order Statistics in Digital Image Processing, Proceedings of the IEEE, 80, 1893 https://doi.org/10.1109/5.192071
  16. Preminger, D. G., Walton, S. R., & Chapman, G. A. 2001, Solar Feature Identification Using Contrasts and Contiguity, Solar physics, 202, 53 https://doi.org/10.1023/A:1011896413891
  17. Serra, J. 1982, Image Analysis and Mathematical Morphology (London: Academic Press)
  18. Veronig, A., Steinegger, M., Otruba, W., Hanslmeier, A., Messerotti, M., Temmer, M., Brunner, G., & Gonzi, S. 2000, Automatic Image Segmentation and Feature Detection in Solar Full-Disk Images. The Solar Cycle and Terrestrial Climate, Solar and Space Weather, 455
  19. Vincent, L. 1994, Morphological Area Openings and Closings for Grey-Scale Images, Nato Asi Series of Computer and Systems Sciences, 126, 197
  20. Walton, S., Chapman, G., Cookson, A., Dobias, J., & Preminger, D. 1998, Processing Photometric Full-Disk Solar Images, Solar Physics, 179, 31 https://doi.org/10.1023/A:1005070932205
  21. Wang, Z., Bovik, A. C., Sheikh, H. R., & Simoncelli, E. P. 2004, Image Quality Assessment: From Error Visibility to Structural Similarity, Image Processing, IEEE Transactions on, 13, 600 https://doi.org/10.1109/TIP.2003.819861
  22. Wang, X., Su, J., & Zhang, H. 2008, The Non-Uniform Pattern in Full-Disc Vector Magnetograms and Its Correction, MNRAS, 387, 1463 https://doi.org/10.1111/j.1365-2966.2008.13346.x
  23. Zharkov, S., Zharkova, V., Ipson, S., & Benkhalil, A. 2005, Technique for Automated Recognition of Sunspots on Full-Disk Solar Images, Eurasip Journal on Applied Signal Processing, 2573
  24. Zharkova, V., Ipson, S., Zharkov, S., Benkhalil, A., Aboudarham, J., & Bentley, R. 2003, A Full-Disk Image Standardisation of the Synoptic Solar Observations at the Meudon Observatory, Solar physics, 214, 89 https://doi.org/10.1023/A:1024081931946

Cited by

  1. Automated removal of stripe interference in full-disk solar images vol.16, pp.6, 2016, https://doi.org/10.1088/1674-4527/16/6/087
  2. Automated detecting and removing cloud shadows in full-disk solar images vol.32, 2014, https://doi.org/10.1016/j.newast.2014.03.006