Star images obtained by star sensors have a low signal-to-noise ratio due to various physical constraints. Low resolution also causes stellar centroid extraction error when traditional methods such as the Gaussian filter or adaptive median filter are utilised to de-noise star images. An automatic centroid extraction method for noisy low-resolution star images is proposed in this study. First, sparse representation is utilised to de-noise the Poisson–Gaussian mixed noise of the low-resolution star image. A high-resolution star image is then reconstructed by using the low-resolution sparse coefficient. Finally, the stellar centroids are extracted automatically by learning the relationship between the high-resolution star image and corresponding stellar centroid image. Experimental results indicate that the positioning accuracy of the stellar centroids is also greatly enhanced by the reconstructed high-resolution stellar centroid image. The correct rate of stellar centroid recognition is 99.35%; the positioning accuracy of stellar centroid and computing time are 16.21″ and 11.30 ms, respectively. The probability distributions ofPoisson and Gaussian noises are 0.50 and 0.08, respectively, while the proposed method correctly recognises stellar centroids at a rate of 76.56%. The results presented here may provide a workable foundation for accurate attitude calculations of the celestial navigation system.

References

1. 1)
  - 15. Yadav, S.K., Sinha, R., Bora, P.K.: ‘Electrocardiogram signal denoising using non-local wavelet transform domain filtering’, IET Signal Process., 2015, 9, (1), pp. 88–96.
2. 2)
  - 11. Vicas, C., Nedevschi, S.: ‘Detecting curvilinear features using structure tensors’, IEEE Trans. Image Process. A Publ. IEEE Signal Process. Soc., 2015, 24, (11), pp. 3874–3887.
3. 3)
  - 17. Gai, S., Wang, L., Yang, G., et al: ‘Sparse representation based on vector extension of reduced quaternion matrix for multiscale image denoising’, IET Image Process., 2016, 10, (8), pp. 598–607.
4. 4)
  - 4. Luo, L., Xu, L., Zhang, H.: ‘Improved centroid extraction algorithm for autonomous star sensor’, IET Image Process., 2015, 9, (10), pp. 901–907.
5. 5)
  - 23. Whipple, F.L.: ‘Smithsonian astrophysical observatory star catalog’, Philos. Trans. R. Soc. A Math. Phys. Eng. Sci., 1966, 284, (1326), pp. 431–442.
6. 6)
  - 2. Yang, S., Yang, G., Zhu, Z., et al: ‘Stellar refraction-based SINS/CNS integrated navigation system for aerospace vehicles’, J. Aerosp. Eng., 2015, 29, (2), pp. 1–11(04015051).
7. 7)
  - 16. Zeng, W., Lu, X., Tan, X.: ‘Non-linear fourth-order telegraph-diffusion equation for noise removal’, IET Image Process., 2013, 7, (7), pp. 335–342.
8. 8)
  - 26. Aharon, M., Elad, M., Bruckstein, A..: ‘K-SVD: an algorithm for designing overcomplete dictionaries for sparse representation’, IEEE Trans. Signal Process, 2006, 54, (11), pp. 4311–4322.
9. 9)
  - 18. Zhou, M., Shi, Y., Yang, J.: ‘Denoising star map data via sparse representation and dictionary learning’, Optik - Int. J. Light Electron Opt., 2015, 126, (11), pp. 1133–1137.
10. 10)
  - 1. Ning, X., Liu, L.: ‘A two-mode INS/CNS navigation method for lunar rovers’, IEEE Trans. Instrum. Meas., 2014, 63, (9), pp. 2170–2179.
11. 11)
  - 8. Massinas, B.A., Doulamis, A., Doulamis, N., et al: ‘Deep convolutional neural networks for modeling patterns of spaceborne interferometric sar systems signals’. AIAA SPACE and Astronautics Forum and Exposition, Florida, United States, September 2017.
12. 12)
  - 6. Zhang, G., Jie, J., Yan, J., et al: ‘Star centroiding error compensation for intensified star sensors’, Opt. Express, 2016, 24, (26), pp. 29830–29842.
13. 13)
  - 31. Bhateja, V., Verma, A., Rastogi, K., et al: ‘A non-iterative adaptive median filter for image denoising’. IEEE Int. Conf. Signal Processing and Integrated Networks, Noida, India, February 2014, pp. 113–118.
14. 14)
  - 29. Saheba, S.M., Upadhyaya, T.K., Sharma, R.K.: ‘Lunar surface crater topology generation using adaptive edge detection algorithm’, IET Image Process., 2016, 10, (9), pp. 657–661.
15. 15)
  - 22. Mcaloon, K.J.: ‘Design considerations for imaging charge-coupled device (ICCD) star sensors’. Proc. of SPIE - The Int. Society for Optical Engineering, Columbia, United States, October 1981, pp. 186–202.
16. 16)
  - 9. Odewahn, S.C., Stockwell, E.B., Pennington, R.L., et al: ‘Automated star/galaxy discrimination with neural networks’, Astron. J., 1992, 103, (1), pp. 318–331.
17. 17)
  - 20. Luisier, F., Blu, T., Unser, M.: ‘Image denoising in mixed Poisson-Gaussian noise’, IEEE Trans. Image Process. A Publ. IEEE Signal Process. Soc., 2011, 20, (3), pp. 696–708.
18. 18)
  - 30. Li, H., Zhang, L., Hong, C., et al: ‘A method of stellar-inertial attitude determination under high dynamic condition’, J. Beijing Univ. Aeronaut. Astronaut., 2014, 40, (12), pp. 1767–1772.
19. 19)
  - 21. Stone, R.C.: ‘A comparison of digital centering algorithms’, Astron. J., 1989, 97, (4), pp. 1227–1237.
20. 20)
  - 5. Zhou, F., Zhao, J., Ye, T., et al: ‘Fast star centroid extraction algorithm with sub-pixel accuracy based on FPGA’, J. Real-Time Image Process., 2014, 12, (3), pp. 1–10.
21. 21)
  - 10. Cao, J., Wang, M., Shi, H., et al: ‘A new approach for large-scale scene image retrieval based on improved parallel K-means algorithm in MapReduce environment’, Math. Probl. Eng., 2016, 2016, (6), pp. 1–17.
22. 22)
  - 32. Yadav, P., Singh, P.: ‘Color impulse noise removal by modified alpha trimmed median mean filter for FVIN’. IEEE Int. Conf. Computational Intelligence and Computing Research, Madurai, India, December 2015, pp. 1–8.
23. 23)
  - 33. Xu, W., Li, Q., Feng, H.J., et al: ‘A novel star image thresholding method for effective segmentation and centroid statistics’. Optik - Int. J. Light Electron Opt., 2013, 124, (20), pp. 4673–4677.
24. 24)
  - 25. Wang, H.Y., Fei, Z.H., Wang, X.L.: ‘Precise simulation of star spots and centroid calculation based on Gaussian distribution’, Opt. Precis. Eng., 2009, 17, (7), pp. 1672–1677.
25. 25)
  - 19. Yang, S., Lee, B.U.: ‘Poisson-Gaussian noise reduction using the hidden Markov model in contourlet domain for fluorescence microscopy images’, Plos One, 2015, 10, (9), pp. 1–19(0136964).
26. 26)
  - 3. Wang, R., Xiong, Z., Liu, J., et al: ‘A new tightly-coupled INS/CNS integrated navigation algorithm with weighted multi-stars observations’, Proc. Inst. Mech. Eng. Part G J. Aerospace. Eng., 2016, 230, (4), pp. 698–712.
27. 27)
  - 13. Fabijańska, A., Sankowski, D.: ‘Noise adaptive switching median-based filter for impulse noise removal from extremely corrupted images’, IET Image Process., 2011, 5, (5), pp. 472–480.
28. 28)
  - 7. Giancarmine, F., Giancarlo, R., Domenico, A., et al: ‘Satellite angular velocity estimation based on star images and optical flow techniques’, Sensors, 2013, 13, (10), pp. 12771–12793.
29. 29)
  - 24. Lu, J., Lei, C., Yang, Y., et al: ‘In-motion initial alignment and positioning with INS/CNS/ODO integrated navigation system for lunar rovers’, Adv. Space Res., 2017, 59, (12), pp. 3070–3079.
30. 30)
  - 14. Schulz, T.J.: ‘Multiframe blind deconvolution of astronomical images’, J. Opt. Soc. Am. A, 1993, 10, (5), pp. 1064–1073.
31. 31)
  - 27. Li, Y., Gong, M., Jiao, L., et al: ‘Change-detection map learning using matching pursuit’, IEEE Trans. Geosci. Remote Sensing, 2015, 53, (8), pp. 4712–4723.
32. 32)
  - 12. Bonetto, M., Carrato, S., Fenu, G., et al: ‘Image processing issues in a social assistive system for the blind’. Int. Symp. Image and Signal Processing and Analysis, Jeju Island, Korea, November 2015, pp. 216–221.
33. 33)
  - 28. Pati, Y.C., Rezaiifar, R., Krishnaprasad, P.S.: ‘Orthogonal matching pursuit: recursive function approximation with applications to wavelet decomposition’. Proc. of the 27th Annual Asilomar Conf. Signals, Systems, and Computers, 1993, vol. 1, pp. 40–44.

Automatic centroid extraction method for noisy star image

References

Related content