Abstract

In this paper, the night sky star image processing algorithm, consisting of image preprocessing, star pattern recognition, and centroiding steps, is improved. It is shown that the proposed noise reduction approach can preserve more necessary information than other frequently used approaches. It is also shown that the proposed thresholding method unlike commonly used techniques can properly perform image binarization, especially in images with uneven illumination. Moreover, the higher performance rate and lower average centroiding estimation error of near 0.045 for 400 simulated images compared to other algorithms show the high capability of the proposed night sky star image processing algorithm.

© 2014 Optical Society of America

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    [CrossRef]
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  4. S. B. Howell, Handbook of CCD Astronomy (Cambridge University, 2006).
  5. Y. Hayosang, “A star recognition algorithm on dynamic environment,” Master’s thesis (KAIST, 2010).
  6. Q. Wei and Z. Weina, “Restoration of motion-blurred star image based on Wiener filter,” in Fourth International Conference on Intelligent Computation Technology and Automation, Shenzhen, Guangdong (2011).
  7. M. A. Samaan, “Toward faster and more accurate star sensors using recursive centroiding and star identification,” Ph.D. dissertation (Texas A&M University, 2003).
  8. R. C. Gonzalez and R. E. Woods, Digital Image Processing, 3rd ed. (Prentice-Hall, 2008).
  9. P. D. Wellner, “Adaptive thresholding for the digitaldesk,” (EuroPARC, 1993).
  10. N. Ma, D. G. Bailey, and C. T. Johnston, “Optimised single pass connected components analysis,” in IEEE International Conference on Field Programmable Technology, Taipei (2008).
  11. K. Suzuki, I. Horiba, and N. Sugieb, “Linear-time connected-component labeling based on sequential local operations,” Comput. Vis. Image Underst. 89, 1–23 (2003).
    [CrossRef]
  12. R. Walczyk, A. Armitage, and T. D. Binnie, “Comparative study on connected component labeling algorithms for embedded video processing systems,” in Proceedings of the 2010 International Conference on Image Processing, Computer Vision, and Pattern Recognition (IPCV) (CSREA, 2010).
  13. R. Haralick, “Some neighborhood operations,” in Real Time Parallel Computing: Image Analysis (Plenum, 1981), pp. 11–35.
  14. D. Bailey and C. Johnston, “Single pass connected components analysis,” in Proceedings of Image and Vision ComputingHamilton, New Zealand (2008), pp. 282–287.
  15. A. Vyas, M. B. Roopashree, and B. R. Prasad, “Performance of centroiding algorithms at low light level conditions in adaptive optics,” in International Conference on Advances in Recent Technologies in Communication and Computing, Kerala, India (2009).
  16. M. Knutson and D. Miller, “Fast star tracker centroid algorithm for high performance CubeSat with air bearing validation,” Master’s thesis (Massachusetts Institute of Technology, 2012).
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    [CrossRef]
  18. C. Fosu, G. W. Hei, and B. Eissfeller, “Determination of centroid of CCD star images,” Int. Arch. Photogram. Remote Sens. Spatial Inform. Sci. 35, 612–617 (2004).
  19. F. Wu, W. Shen, J. Zhou, and X. Chen, “Design and simulation of a novel APS star tracker,” in International Conference on Optical Instruments and Technology: Optical Systems and Optoelectronic Instruments (2008).
  20. J. Shen, G. Zhang, and X. Wei, “Simulation analysis of dynamic working performance for star trackers,” J. Opt. Soc. Am. A 27, 2638–2647 (2010).
    [CrossRef]
  21. S. M. Mohammadi, M. S. Helfroush, and K. Kazemi, “Novel shape-texture feature extraction for medical x-ray image classification,” Int. J. Innovative Comput. Inform. Control 8, 659–676 (2012).
  22. B. M. Quine, V. Tarasyuk, H. Mebrahtu, and R. Hornsey, “Determining star-image location: a new sub-pixel interpolation technique to process image centroids,” Comput. Phys. Commun. 177, 700–706 (2007).
    [CrossRef]

2012

S. M. Mohammadi, M. S. Helfroush, and K. Kazemi, “Novel shape-texture feature extraction for medical x-ray image classification,” Int. J. Innovative Comput. Inform. Control 8, 659–676 (2012).

2011

2010

2008

M. Kolomenkin, S. Polak, I. Shimshoni, and M. Lindenbaum, “Geometric voting algorithm for star trackers,” IEEE Trans. Aerosp. Electron. Syst. 44, 441–456 (2008).
[CrossRef]

2007

B. M. Quine, V. Tarasyuk, H. Mebrahtu, and R. Hornsey, “Determining star-image location: a new sub-pixel interpolation technique to process image centroids,” Comput. Phys. Commun. 177, 700–706 (2007).
[CrossRef]

2004

C. Fosu, G. W. Hei, and B. Eissfeller, “Determination of centroid of CCD star images,” Int. Arch. Photogram. Remote Sens. Spatial Inform. Sci. 35, 612–617 (2004).

2003

K. Suzuki, I. Horiba, and N. Sugieb, “Linear-time connected-component labeling based on sequential local operations,” Comput. Vis. Image Underst. 89, 1–23 (2003).
[CrossRef]

Armitage, A.

R. Walczyk, A. Armitage, and T. D. Binnie, “Comparative study on connected component labeling algorithms for embedded video processing systems,” in Proceedings of the 2010 International Conference on Image Processing, Computer Vision, and Pattern Recognition (IPCV) (CSREA, 2010).

Bailey, D.

D. Bailey and C. Johnston, “Single pass connected components analysis,” in Proceedings of Image and Vision ComputingHamilton, New Zealand (2008), pp. 282–287.

Bailey, D. G.

N. Ma, D. G. Bailey, and C. T. Johnston, “Optimised single pass connected components analysis,” in IEEE International Conference on Field Programmable Technology, Taipei (2008).

Berry, R.

R. Berry and J. Burnell, The Handbook of Astronomical Image Processing, 2nd ed. (Willmann-Bell, 2005).

Binnie, T. D.

R. Walczyk, A. Armitage, and T. D. Binnie, “Comparative study on connected component labeling algorithms for embedded video processing systems,” in Proceedings of the 2010 International Conference on Image Processing, Computer Vision, and Pattern Recognition (IPCV) (CSREA, 2010).

Burnell, J.

R. Berry and J. Burnell, The Handbook of Astronomical Image Processing, 2nd ed. (Willmann-Bell, 2005).

Chen, X.

F. Wu, W. Shen, J. Zhou, and X. Chen, “Design and simulation of a novel APS star tracker,” in International Conference on Optical Instruments and Technology: Optical Systems and Optoelectronic Instruments (2008).

Eissfeller, B.

C. Fosu, G. W. Hei, and B. Eissfeller, “Determination of centroid of CCD star images,” Int. Arch. Photogram. Remote Sens. Spatial Inform. Sci. 35, 612–617 (2004).

Fosu, C.

C. Fosu, G. W. Hei, and B. Eissfeller, “Determination of centroid of CCD star images,” Int. Arch. Photogram. Remote Sens. Spatial Inform. Sci. 35, 612–617 (2004).

Gonzalez, R. C.

R. C. Gonzalez and R. E. Woods, Digital Image Processing, 3rd ed. (Prentice-Hall, 2008).

Haralick, R.

R. Haralick, “Some neighborhood operations,” in Real Time Parallel Computing: Image Analysis (Plenum, 1981), pp. 11–35.

Hayosang, Y.

Y. Hayosang, “A star recognition algorithm on dynamic environment,” Master’s thesis (KAIST, 2010).

Hei, G. W.

C. Fosu, G. W. Hei, and B. Eissfeller, “Determination of centroid of CCD star images,” Int. Arch. Photogram. Remote Sens. Spatial Inform. Sci. 35, 612–617 (2004).

Helfroush, M. S.

S. M. Mohammadi, M. S. Helfroush, and K. Kazemi, “Novel shape-texture feature extraction for medical x-ray image classification,” Int. J. Innovative Comput. Inform. Control 8, 659–676 (2012).

Horiba, I.

K. Suzuki, I. Horiba, and N. Sugieb, “Linear-time connected-component labeling based on sequential local operations,” Comput. Vis. Image Underst. 89, 1–23 (2003).
[CrossRef]

Hornsey, R.

B. M. Quine, V. Tarasyuk, H. Mebrahtu, and R. Hornsey, “Determining star-image location: a new sub-pixel interpolation technique to process image centroids,” Comput. Phys. Commun. 177, 700–706 (2007).
[CrossRef]

Howell, S. B.

S. B. Howell, Handbook of CCD Astronomy (Cambridge University, 2006).

Huffman, K. M.

K. M. Huffman, “Designing star trackers to meet micro-satellite requirements,” Master’s thesis (Massachusetts Institute of Technology, 2006).

Johnston, C.

D. Bailey and C. Johnston, “Single pass connected components analysis,” in Proceedings of Image and Vision ComputingHamilton, New Zealand (2008), pp. 282–287.

Johnston, C. T.

N. Ma, D. G. Bailey, and C. T. Johnston, “Optimised single pass connected components analysis,” in IEEE International Conference on Field Programmable Technology, Taipei (2008).

Kazemi, K.

S. M. Mohammadi, M. S. Helfroush, and K. Kazemi, “Novel shape-texture feature extraction for medical x-ray image classification,” Int. J. Innovative Comput. Inform. Control 8, 659–676 (2012).

Knutson, M.

M. Knutson and D. Miller, “Fast star tracker centroid algorithm for high performance CubeSat with air bearing validation,” Master’s thesis (Massachusetts Institute of Technology, 2012).

Kolomenkin, M.

M. Kolomenkin, S. Polak, I. Shimshoni, and M. Lindenbaum, “Geometric voting algorithm for star trackers,” IEEE Trans. Aerosp. Electron. Syst. 44, 441–456 (2008).
[CrossRef]

Li, X.

Lindenbaum, M.

M. Kolomenkin, S. Polak, I. Shimshoni, and M. Lindenbaum, “Geometric voting algorithm for star trackers,” IEEE Trans. Aerosp. Electron. Syst. 44, 441–456 (2008).
[CrossRef]

Liu, H.

Ma, N.

N. Ma, D. G. Bailey, and C. T. Johnston, “Optimised single pass connected components analysis,” in IEEE International Conference on Field Programmable Technology, Taipei (2008).

Mebrahtu, H.

B. M. Quine, V. Tarasyuk, H. Mebrahtu, and R. Hornsey, “Determining star-image location: a new sub-pixel interpolation technique to process image centroids,” Comput. Phys. Commun. 177, 700–706 (2007).
[CrossRef]

Miller, D.

M. Knutson and D. Miller, “Fast star tracker centroid algorithm for high performance CubeSat with air bearing validation,” Master’s thesis (Massachusetts Institute of Technology, 2012).

Mohammadi, S. M.

S. M. Mohammadi, M. S. Helfroush, and K. Kazemi, “Novel shape-texture feature extraction for medical x-ray image classification,” Int. J. Innovative Comput. Inform. Control 8, 659–676 (2012).

Polak, S.

M. Kolomenkin, S. Polak, I. Shimshoni, and M. Lindenbaum, “Geometric voting algorithm for star trackers,” IEEE Trans. Aerosp. Electron. Syst. 44, 441–456 (2008).
[CrossRef]

Prasad, B. R.

A. Vyas, M. B. Roopashree, and B. R. Prasad, “Performance of centroiding algorithms at low light level conditions in adaptive optics,” in International Conference on Advances in Recent Technologies in Communication and Computing, Kerala, India (2009).

Quine, B. M.

B. M. Quine, V. Tarasyuk, H. Mebrahtu, and R. Hornsey, “Determining star-image location: a new sub-pixel interpolation technique to process image centroids,” Comput. Phys. Commun. 177, 700–706 (2007).
[CrossRef]

Roopashree, M. B.

A. Vyas, M. B. Roopashree, and B. R. Prasad, “Performance of centroiding algorithms at low light level conditions in adaptive optics,” in International Conference on Advances in Recent Technologies in Communication and Computing, Kerala, India (2009).

Samaan, M. A.

M. A. Samaan, “Toward faster and more accurate star sensors using recursive centroiding and star identification,” Ph.D. dissertation (Texas A&M University, 2003).

Shen, J.

Shen, W.

F. Wu, W. Shen, J. Zhou, and X. Chen, “Design and simulation of a novel APS star tracker,” in International Conference on Optical Instruments and Technology: Optical Systems and Optoelectronic Instruments (2008).

Shimshoni, I.

M. Kolomenkin, S. Polak, I. Shimshoni, and M. Lindenbaum, “Geometric voting algorithm for star trackers,” IEEE Trans. Aerosp. Electron. Syst. 44, 441–456 (2008).
[CrossRef]

Sugieb, N.

K. Suzuki, I. Horiba, and N. Sugieb, “Linear-time connected-component labeling based on sequential local operations,” Comput. Vis. Image Underst. 89, 1–23 (2003).
[CrossRef]

Suzuki, K.

K. Suzuki, I. Horiba, and N. Sugieb, “Linear-time connected-component labeling based on sequential local operations,” Comput. Vis. Image Underst. 89, 1–23 (2003).
[CrossRef]

Tan, J.

Tarasyuk, V.

B. M. Quine, V. Tarasyuk, H. Mebrahtu, and R. Hornsey, “Determining star-image location: a new sub-pixel interpolation technique to process image centroids,” Comput. Phys. Commun. 177, 700–706 (2007).
[CrossRef]

Vyas, A.

A. Vyas, M. B. Roopashree, and B. R. Prasad, “Performance of centroiding algorithms at low light level conditions in adaptive optics,” in International Conference on Advances in Recent Technologies in Communication and Computing, Kerala, India (2009).

Walczyk, R.

R. Walczyk, A. Armitage, and T. D. Binnie, “Comparative study on connected component labeling algorithms for embedded video processing systems,” in Proceedings of the 2010 International Conference on Image Processing, Computer Vision, and Pattern Recognition (IPCV) (CSREA, 2010).

Wang, J.

Wei, Q.

Q. Wei and Z. Weina, “Restoration of motion-blurred star image based on Wiener filter,” in Fourth International Conference on Intelligent Computation Technology and Automation, Shenzhen, Guangdong (2011).

Wei, X.

Weina, Z.

Q. Wei and Z. Weina, “Restoration of motion-blurred star image based on Wiener filter,” in Fourth International Conference on Intelligent Computation Technology and Automation, Shenzhen, Guangdong (2011).

Wellner, P. D.

P. D. Wellner, “Adaptive thresholding for the digitaldesk,” (EuroPARC, 1993).

Woods, R. E.

R. C. Gonzalez and R. E. Woods, Digital Image Processing, 3rd ed. (Prentice-Hall, 2008).

Wu, F.

F. Wu, W. Shen, J. Zhou, and X. Chen, “Design and simulation of a novel APS star tracker,” in International Conference on Optical Instruments and Technology: Optical Systems and Optoelectronic Instruments (2008).

Yang, J.

Zhang, G.

Zhou, J.

F. Wu, W. Shen, J. Zhou, and X. Chen, “Design and simulation of a novel APS star tracker,” in International Conference on Optical Instruments and Technology: Optical Systems and Optoelectronic Instruments (2008).

Appl. Opt.

Comput. Phys. Commun.

B. M. Quine, V. Tarasyuk, H. Mebrahtu, and R. Hornsey, “Determining star-image location: a new sub-pixel interpolation technique to process image centroids,” Comput. Phys. Commun. 177, 700–706 (2007).
[CrossRef]

Comput. Vis. Image Underst.

K. Suzuki, I. Horiba, and N. Sugieb, “Linear-time connected-component labeling based on sequential local operations,” Comput. Vis. Image Underst. 89, 1–23 (2003).
[CrossRef]

IEEE Trans. Aerosp. Electron. Syst.

M. Kolomenkin, S. Polak, I. Shimshoni, and M. Lindenbaum, “Geometric voting algorithm for star trackers,” IEEE Trans. Aerosp. Electron. Syst. 44, 441–456 (2008).
[CrossRef]

Int. Arch. Photogram. Remote Sens. Spatial Inform. Sci.

C. Fosu, G. W. Hei, and B. Eissfeller, “Determination of centroid of CCD star images,” Int. Arch. Photogram. Remote Sens. Spatial Inform. Sci. 35, 612–617 (2004).

Int. J. Innovative Comput. Inform. Control

S. M. Mohammadi, M. S. Helfroush, and K. Kazemi, “Novel shape-texture feature extraction for medical x-ray image classification,” Int. J. Innovative Comput. Inform. Control 8, 659–676 (2012).

J. Opt. Soc. Am. A

Other

F. Wu, W. Shen, J. Zhou, and X. Chen, “Design and simulation of a novel APS star tracker,” in International Conference on Optical Instruments and Technology: Optical Systems and Optoelectronic Instruments (2008).

R. Walczyk, A. Armitage, and T. D. Binnie, “Comparative study on connected component labeling algorithms for embedded video processing systems,” in Proceedings of the 2010 International Conference on Image Processing, Computer Vision, and Pattern Recognition (IPCV) (CSREA, 2010).

R. Haralick, “Some neighborhood operations,” in Real Time Parallel Computing: Image Analysis (Plenum, 1981), pp. 11–35.

D. Bailey and C. Johnston, “Single pass connected components analysis,” in Proceedings of Image and Vision ComputingHamilton, New Zealand (2008), pp. 282–287.

A. Vyas, M. B. Roopashree, and B. R. Prasad, “Performance of centroiding algorithms at low light level conditions in adaptive optics,” in International Conference on Advances in Recent Technologies in Communication and Computing, Kerala, India (2009).

M. Knutson and D. Miller, “Fast star tracker centroid algorithm for high performance CubeSat with air bearing validation,” Master’s thesis (Massachusetts Institute of Technology, 2012).

R. Berry and J. Burnell, The Handbook of Astronomical Image Processing, 2nd ed. (Willmann-Bell, 2005).

K. M. Huffman, “Designing star trackers to meet micro-satellite requirements,” Master’s thesis (Massachusetts Institute of Technology, 2006).

S. B. Howell, Handbook of CCD Astronomy (Cambridge University, 2006).

Y. Hayosang, “A star recognition algorithm on dynamic environment,” Master’s thesis (KAIST, 2010).

Q. Wei and Z. Weina, “Restoration of motion-blurred star image based on Wiener filter,” in Fourth International Conference on Intelligent Computation Technology and Automation, Shenzhen, Guangdong (2011).

M. A. Samaan, “Toward faster and more accurate star sensors using recursive centroiding and star identification,” Ph.D. dissertation (Texas A&M University, 2003).

R. C. Gonzalez and R. E. Woods, Digital Image Processing, 3rd ed. (Prentice-Hall, 2008).

P. D. Wellner, “Adaptive thresholding for the digitaldesk,” (EuroPARC, 1993).

N. Ma, D. G. Bailey, and C. T. Johnston, “Optimised single pass connected components analysis,” in IEEE International Conference on Field Programmable Technology, Taipei (2008).

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Figures (7)

Fig. 1.
Fig. 1.

Main parts of night sky image processing.

Fig. 2.
Fig. 2.

(a) Night sky image with considerable uneven illumination. (b) Histogram of this image.

Fig. 3.
Fig. 3.

Splitting the center pixel into four subpixels to find the precise centroid.

Fig. 4.
Fig. 4.

New subpixel centroid (Oi+1) with eight-neighbor subpixels (a1,,a4 and b1,,b4).

Fig. 5.
Fig. 5.

Four types of simulated noisy images with Gaussian random noise with (a) σ=0.5; (b) σ=1; (c) σ=2; and (d) σ=4.

Fig. 6.
Fig. 6.

CEE of proposed centroiding approach applied to a simulated test image with Gaussian noise with σ=4, which was previously denoised by AD, Wiener filter, median filter, averaging filter, and Gaussian filter.

Fig. 7.
Fig. 7.

Thresholding results: (a) original real image; (b) after thresholding by Berry and Burnell global method [2]; (c) after thresholding by Gonzalez and Woods global method [8]: (d) after thresholding by Gonzalez and Woods adaptive local method [8]; and (e) after thresholding by proposed automatic adaptive local method.

Tables (2)

Tables Icon

Table 1. Average CEE (Pixels) of Different Centroiding Approaches Applied to 400 Noisy Images (100 Images for each Standard Deviation)

Tables Icon

Table 2. Computation Time of Four Centroiding Approaches Applied to a Simulated 1024×1024 Image

Equations (14)

Equations on this page are rendered with MathJax. Learn more.

It=div(c(x,y,t)I)=c·I+c(x,y,t)2I,
c(I)=e(I/K)2,
c(I)=11+(IK)2,
R(x,y)=I(x,y)μW(x,y),
T=minR+maxR2,
Ix=y=1LI(x,y),
Jy=x=1LI(x,y),
I¯=12Lx=1LIx,
J¯=12Ly=1LJy.
xc=x=1L(IxI¯)xx=1L(IxI¯),IxI¯>0,
yc=y=1L(JyJ¯)yy=1L(JyJ¯),JyJ¯>0,
sp1=a1+a2+2Oi4,sp2=a2+a3+2Oi4,sp3=a3+a4+2Oi4,sp4=a4+a1+2Oi4,
a1N=b1N+Oi+2a14,a2N=b1N+Oi+2a24,a3N=sp2,a4N=sp4,
b1N=a1+a2+2b14,b2N=Oi+b2+2a24,b3N=sp3,b4N=b4+Oi+2a14.

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