Abstract

Video stabilization in atmosphere turbulent conditions is aimed at removing spatiotemporally varying distortions from video recordings. Conventional shaky video stabilization approaches do not perform effectively under turbulent circumstances due to the erratic motion common to those conditions. Using complex-valued image pyramids, we propose a method to mitigate this erratic motion in videos. First, each frame of a video is decomposed into different spatial frequencies using the Laplacian pyramid. Second, a Riesz transform is adopted to extract the local amplitude and the local phase of each sub-band. Next, low-pass filters are designed to attenuate the local amplitude and phase variations to remove turbulence-induced distortions. Experimental results show that the proposed approach is efficient and provides stabilizing video in atmosphere turbulent conditions.

© 2016 Optical Society of America

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References

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    [Crossref]
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    [Crossref]
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    [Crossref]

2016 (1)

2015 (1)

P. Ruiz, X. Zhou, J. Mateos, R. Molina, and A. K. Katsaggelos, “Variational Bayesian Blind Image Deconvolution: A review,” Digit. Signal Process. 47, 116–127 (2015).
[Crossref]

2014 (1)

R. Gal, N. Kiryati, and N. Sochen, “Progress in the restoration of image sequences degraded by atmospheric turbulence,” Pattern Recognit. Lett. 48, 8–14 (2014).
[Crossref]

2013 (5)

N. Anantrasirichai, A. Achim, N. G. Kingsbury, and D. R. Bull, “Atmospheric turbulence mitigation using complex wavelet-based fusion,” IEEE Trans. Image Process. 22(6), 2398–2408 (2013).
[Crossref] [PubMed]

X. Zhu and P. Milanfar, “Removing atmospheric Turbulence via Space-Invariant Deconvolution,” IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 157–170 (2013).
[Crossref] [PubMed]

O. Oreifej, X. Li, and M. Shah, “Simultaneous video stabilization and moving object detection in turbulence,” IEEE Trans. Pattern Anal. Mach. Intell. 35(2), 450–462 (2013).
[Crossref] [PubMed]

Y. Lou, S. H. Kang, S. Soatto, and A. L. Bertozzi, “Video stabilization of atmospheric turbulence distortion,” Inverse Probl. Imaging (Springfield) 7(3), 839–861 (2013).
[Crossref]

N. Wadhwa, M. Rubinstein, F. Durand, and W. T. Freeman, “Phase-based video motion processing,” ACM TOG 32(4), 80 (2013).
[Crossref]

2011 (2)

E. Repasi and R. Weiss, “Computer Simulation of Image Degradations by Atmospheric Turbulence for Horizontal Views,” Proc. SPIE 8014, 80140U (2011).
[Crossref]

M. Loktev, O. Soloviev, S. Savenko, and G. Vdovin, “Speckle imaging through turbulent atmosphere based on adaptable pupil segmentation,” Opt. Lett. 36(14), 2656–2658 (2011).
[Crossref] [PubMed]

2010 (1)

X. Zhu and P. Milanfar, “Image reconstruction from videos distorted by atmospheric turbulence,” Proc. SPIE 7543(1), 423–426 (2010).

2008 (1)

E. Repasi and R. Weiss, “Analysis of Image Distortions by Atmospheric Turbulence and Computer Simulation of Turbulence Effects,” Proc. SPIE 6941, 69410S (2008).
[Crossref]

2007 (2)

L. Yaroslavsky, B. Fishbain, G. Shabat, and I. Ideses, “Superresolution in turbulent videos: making profit from damage,” Opt. Lett. 32(20), 3038–3040 (2007).
[Crossref] [PubMed]

D. Li, R. M. Mersereau, and S. Simske, “Atmospheric turbulence-degraded image restoration using principal components analysis,” IEEE Geosci. Remote Sens. Lett. 4(3), 340–344 (2007).
[Crossref]

2001 (1)

M. Felsberg and G. Sommer, “The monogenic signal,” IEEE Trans. Signal Process. 49(12), 3136–3144 (2001).
[Crossref]

1999 (1)

P. Kovesi, “Image features from phase congruency,” J. Computer Vis. Res. 1(3), 1–26 (1999).

1995 (1)

1987 (1)

M. C. Morrone and R. A. Owens, “Feature detection from local energy,” Pattern Recognit. Lett. 6(5), 303–313 (1987).
[Crossref]

1983 (1)

P. J. Burt and E. H. Shashua, “The laplacian pyramid as a compact image code,” IEEE Trans. Commun. 31(4), 532–540 (1983).
[Crossref]

Achim, A.

N. Anantrasirichai, A. Achim, N. G. Kingsbury, and D. R. Bull, “Atmospheric turbulence mitigation using complex wavelet-based fusion,” IEEE Trans. Image Process. 22(6), 2398–2408 (2013).
[Crossref] [PubMed]

Anantrasirichai, N.

N. Anantrasirichai, A. Achim, N. G. Kingsbury, and D. R. Bull, “Atmospheric turbulence mitigation using complex wavelet-based fusion,” IEEE Trans. Image Process. 22(6), 2398–2408 (2013).
[Crossref] [PubMed]

Bertozzi, A. L.

Y. Lou, S. H. Kang, S. Soatto, and A. L. Bertozzi, “Video stabilization of atmospheric turbulence distortion,” Inverse Probl. Imaging (Springfield) 7(3), 839–861 (2013).
[Crossref]

Bull, D. R.

N. Anantrasirichai, A. Achim, N. G. Kingsbury, and D. R. Bull, “Atmospheric turbulence mitigation using complex wavelet-based fusion,” IEEE Trans. Image Process. 22(6), 2398–2408 (2013).
[Crossref] [PubMed]

Burt, P. J.

P. J. Burt and E. H. Shashua, “The laplacian pyramid as a compact image code,” IEEE Trans. Commun. 31(4), 532–540 (1983).
[Crossref]

Dror, I.

Durand, F.

N. Wadhwa, M. Rubinstein, F. Durand, and W. T. Freeman, “Phase-based video motion processing,” ACM TOG 32(4), 80 (2013).
[Crossref]

Felsberg, M.

M. Felsberg and G. Sommer, “The monogenic signal,” IEEE Trans. Signal Process. 49(12), 3136–3144 (2001).
[Crossref]

Fishbain, B.

Freeman, W. T.

N. Wadhwa, M. Rubinstein, F. Durand, and W. T. Freeman, “Phase-based video motion processing,” ACM TOG 32(4), 80 (2013).
[Crossref]

Gal, R.

R. Gal, N. Kiryati, and N. Sochen, “Progress in the restoration of image sequences degraded by atmospheric turbulence,” Pattern Recognit. Lett. 48, 8–14 (2014).
[Crossref]

Hart, M.

Hope, D. A.

Ideses, I.

Jefferies, S. M.

Kang, S. H.

Y. Lou, S. H. Kang, S. Soatto, and A. L. Bertozzi, “Video stabilization of atmospheric turbulence distortion,” Inverse Probl. Imaging (Springfield) 7(3), 839–861 (2013).
[Crossref]

Katsaggelos, A. K.

P. Ruiz, X. Zhou, J. Mateos, R. Molina, and A. K. Katsaggelos, “Variational Bayesian Blind Image Deconvolution: A review,” Digit. Signal Process. 47, 116–127 (2015).
[Crossref]

Kingsbury, N. G.

N. Anantrasirichai, A. Achim, N. G. Kingsbury, and D. R. Bull, “Atmospheric turbulence mitigation using complex wavelet-based fusion,” IEEE Trans. Image Process. 22(6), 2398–2408 (2013).
[Crossref] [PubMed]

Kiryati, N.

R. Gal, N. Kiryati, and N. Sochen, “Progress in the restoration of image sequences degraded by atmospheric turbulence,” Pattern Recognit. Lett. 48, 8–14 (2014).
[Crossref]

Kopeika, N. S.

Kovesi, P.

P. Kovesi, “Image features from phase congruency,” J. Computer Vis. Res. 1(3), 1–26 (1999).

Li, D.

D. Li, R. M. Mersereau, and S. Simske, “Atmospheric turbulence-degraded image restoration using principal components analysis,” IEEE Geosci. Remote Sens. Lett. 4(3), 340–344 (2007).
[Crossref]

Li, X.

O. Oreifej, X. Li, and M. Shah, “Simultaneous video stabilization and moving object detection in turbulence,” IEEE Trans. Pattern Anal. Mach. Intell. 35(2), 450–462 (2013).
[Crossref] [PubMed]

Lim, J. S.

A. V. Oppenheim and J. S. Lim, “The importance of phase in signals,” InProceedings of the IEEE (IEEE, 1981), 69(5), pp, 529–541.

Loktev, M.

Lou, Y.

Y. Lou, S. H. Kang, S. Soatto, and A. L. Bertozzi, “Video stabilization of atmospheric turbulence distortion,” Inverse Probl. Imaging (Springfield) 7(3), 839–861 (2013).
[Crossref]

Mateos, J.

P. Ruiz, X. Zhou, J. Mateos, R. Molina, and A. K. Katsaggelos, “Variational Bayesian Blind Image Deconvolution: A review,” Digit. Signal Process. 47, 116–127 (2015).
[Crossref]

Mersereau, R. M.

D. Li, R. M. Mersereau, and S. Simske, “Atmospheric turbulence-degraded image restoration using principal components analysis,” IEEE Geosci. Remote Sens. Lett. 4(3), 340–344 (2007).
[Crossref]

Milanfar, P.

X. Zhu and P. Milanfar, “Removing atmospheric Turbulence via Space-Invariant Deconvolution,” IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 157–170 (2013).
[Crossref] [PubMed]

X. Zhu and P. Milanfar, “Image reconstruction from videos distorted by atmospheric turbulence,” Proc. SPIE 7543(1), 423–426 (2010).

Molina, R.

P. Ruiz, X. Zhou, J. Mateos, R. Molina, and A. K. Katsaggelos, “Variational Bayesian Blind Image Deconvolution: A review,” Digit. Signal Process. 47, 116–127 (2015).
[Crossref]

Morrone, M. C.

M. C. Morrone and R. A. Owens, “Feature detection from local energy,” Pattern Recognit. Lett. 6(5), 303–313 (1987).
[Crossref]

Nagy, J. G.

Oppenheim, A. V.

A. V. Oppenheim and J. S. Lim, “The importance of phase in signals,” InProceedings of the IEEE (IEEE, 1981), 69(5), pp, 529–541.

Oreifej, O.

O. Oreifej, X. Li, and M. Shah, “Simultaneous video stabilization and moving object detection in turbulence,” IEEE Trans. Pattern Anal. Mach. Intell. 35(2), 450–462 (2013).
[Crossref] [PubMed]

Owens, R. A.

M. C. Morrone and R. A. Owens, “Feature detection from local energy,” Pattern Recognit. Lett. 6(5), 303–313 (1987).
[Crossref]

Repasi, E.

E. Repasi and R. Weiss, “Computer Simulation of Image Degradations by Atmospheric Turbulence for Horizontal Views,” Proc. SPIE 8014, 80140U (2011).
[Crossref]

E. Repasi and R. Weiss, “Analysis of Image Distortions by Atmospheric Turbulence and Computer Simulation of Turbulence Effects,” Proc. SPIE 6941, 69410S (2008).
[Crossref]

Rubinstein, M.

N. Wadhwa, M. Rubinstein, F. Durand, and W. T. Freeman, “Phase-based video motion processing,” ACM TOG 32(4), 80 (2013).
[Crossref]

Ruiz, P.

P. Ruiz, X. Zhou, J. Mateos, R. Molina, and A. K. Katsaggelos, “Variational Bayesian Blind Image Deconvolution: A review,” Digit. Signal Process. 47, 116–127 (2015).
[Crossref]

Savenko, S.

Shabat, G.

Shah, M.

O. Oreifej, X. Li, and M. Shah, “Simultaneous video stabilization and moving object detection in turbulence,” IEEE Trans. Pattern Anal. Mach. Intell. 35(2), 450–462 (2013).
[Crossref] [PubMed]

Shashua, E. H.

P. J. Burt and E. H. Shashua, “The laplacian pyramid as a compact image code,” IEEE Trans. Commun. 31(4), 532–540 (1983).
[Crossref]

Simske, S.

D. Li, R. M. Mersereau, and S. Simske, “Atmospheric turbulence-degraded image restoration using principal components analysis,” IEEE Geosci. Remote Sens. Lett. 4(3), 340–344 (2007).
[Crossref]

Soatto, S.

Y. Lou, S. H. Kang, S. Soatto, and A. L. Bertozzi, “Video stabilization of atmospheric turbulence distortion,” Inverse Probl. Imaging (Springfield) 7(3), 839–861 (2013).
[Crossref]

Sochen, N.

R. Gal, N. Kiryati, and N. Sochen, “Progress in the restoration of image sequences degraded by atmospheric turbulence,” Pattern Recognit. Lett. 48, 8–14 (2014).
[Crossref]

Soloviev, O.

Sommer, G.

M. Felsberg and G. Sommer, “The monogenic signal,” IEEE Trans. Signal Process. 49(12), 3136–3144 (2001).
[Crossref]

Vdovin, G.

Wadhwa, N.

N. Wadhwa, M. Rubinstein, F. Durand, and W. T. Freeman, “Phase-based video motion processing,” ACM TOG 32(4), 80 (2013).
[Crossref]

Weiss, R.

E. Repasi and R. Weiss, “Computer Simulation of Image Degradations by Atmospheric Turbulence for Horizontal Views,” Proc. SPIE 8014, 80140U (2011).
[Crossref]

E. Repasi and R. Weiss, “Analysis of Image Distortions by Atmospheric Turbulence and Computer Simulation of Turbulence Effects,” Proc. SPIE 6941, 69410S (2008).
[Crossref]

Yaroslavsky, L.

Zhou, X.

P. Ruiz, X. Zhou, J. Mateos, R. Molina, and A. K. Katsaggelos, “Variational Bayesian Blind Image Deconvolution: A review,” Digit. Signal Process. 47, 116–127 (2015).
[Crossref]

Zhu, X.

X. Zhu and P. Milanfar, “Removing atmospheric Turbulence via Space-Invariant Deconvolution,” IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 157–170 (2013).
[Crossref] [PubMed]

X. Zhu and P. Milanfar, “Image reconstruction from videos distorted by atmospheric turbulence,” Proc. SPIE 7543(1), 423–426 (2010).

ACM TOG (1)

N. Wadhwa, M. Rubinstein, F. Durand, and W. T. Freeman, “Phase-based video motion processing,” ACM TOG 32(4), 80 (2013).
[Crossref]

Digit. Signal Process. (1)

P. Ruiz, X. Zhou, J. Mateos, R. Molina, and A. K. Katsaggelos, “Variational Bayesian Blind Image Deconvolution: A review,” Digit. Signal Process. 47, 116–127 (2015).
[Crossref]

IEEE Geosci. Remote Sens. Lett. (1)

D. Li, R. M. Mersereau, and S. Simske, “Atmospheric turbulence-degraded image restoration using principal components analysis,” IEEE Geosci. Remote Sens. Lett. 4(3), 340–344 (2007).
[Crossref]

IEEE Trans. Commun. (1)

P. J. Burt and E. H. Shashua, “The laplacian pyramid as a compact image code,” IEEE Trans. Commun. 31(4), 532–540 (1983).
[Crossref]

IEEE Trans. Image Process. (1)

N. Anantrasirichai, A. Achim, N. G. Kingsbury, and D. R. Bull, “Atmospheric turbulence mitigation using complex wavelet-based fusion,” IEEE Trans. Image Process. 22(6), 2398–2408 (2013).
[Crossref] [PubMed]

IEEE Trans. Pattern Anal. Mach. Intell. (2)

O. Oreifej, X. Li, and M. Shah, “Simultaneous video stabilization and moving object detection in turbulence,” IEEE Trans. Pattern Anal. Mach. Intell. 35(2), 450–462 (2013).
[Crossref] [PubMed]

X. Zhu and P. Milanfar, “Removing atmospheric Turbulence via Space-Invariant Deconvolution,” IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 157–170 (2013).
[Crossref] [PubMed]

IEEE Trans. Signal Process. (1)

M. Felsberg and G. Sommer, “The monogenic signal,” IEEE Trans. Signal Process. 49(12), 3136–3144 (2001).
[Crossref]

Inverse Probl. Imaging (Springfield) (1)

Y. Lou, S. H. Kang, S. Soatto, and A. L. Bertozzi, “Video stabilization of atmospheric turbulence distortion,” Inverse Probl. Imaging (Springfield) 7(3), 839–861 (2013).
[Crossref]

J. Computer Vis. Res. (1)

P. Kovesi, “Image features from phase congruency,” J. Computer Vis. Res. 1(3), 1–26 (1999).

J. Opt. Soc. Am. A (1)

Opt. Express (1)

Opt. Lett. (2)

Pattern Recognit. Lett. (2)

R. Gal, N. Kiryati, and N. Sochen, “Progress in the restoration of image sequences degraded by atmospheric turbulence,” Pattern Recognit. Lett. 48, 8–14 (2014).
[Crossref]

M. C. Morrone and R. A. Owens, “Feature detection from local energy,” Pattern Recognit. Lett. 6(5), 303–313 (1987).
[Crossref]

Proc. SPIE (3)

X. Zhu and P. Milanfar, “Image reconstruction from videos distorted by atmospheric turbulence,” Proc. SPIE 7543(1), 423–426 (2010).

E. Repasi and R. Weiss, “Analysis of Image Distortions by Atmospheric Turbulence and Computer Simulation of Turbulence Effects,” Proc. SPIE 6941, 69410S (2008).
[Crossref]

E. Repasi and R. Weiss, “Computer Simulation of Image Degradations by Atmospheric Turbulence for Horizontal Views,” Proc. SPIE 8014, 80140U (2011).
[Crossref]

Other (7)

O. Haik and Y. Yitzhaky, http://www.ee.bgu.ac.il/~itzik/VideosOE06/

N. Wadhwa, M. Rubinstein, F. Durand, and W. T. Freeman, “SIGGRAPH 2013 Phase-Based Video Motion Processing”, http://people.csail.mit.edu/nwadhwa/phase-video/

R. K. Tyson, Principles of Adaptive Optics (Academic, 1991).

Institute of Astronomy, University of Cambridge, “Lucky Imaging”, http://www.ast.cam.ac.uk/~optics/Lucky_Web_Site/index.htm

F. J. Madrid-Cuevas, R. Medina-Carnicer, Á. Carmona-Poyato, and N. L. Fernández-García, “Dominant Points Detection Using Phase Congruence,” Iberian Conference on Pattern Recognition and Image Analysis (Springer Berlin Heidelberg, 2007), pp.138–145.
[Crossref]

N. Wadhwa, M. Rubinstein, F. Durand, and W. T. Freeman, “Riesz pyramids for fast phase-based video magnification,” In proceedings of ICCP, (IEEE, 2014), pp.1–10.

A. V. Oppenheim and J. S. Lim, “The importance of phase in signals,” InProceedings of the IEEE (IEEE, 1981), 69(5), pp, 529–541.

Supplementary Material (8)

NameDescription
» Visualization 1: MP4 (1663 KB)      The effects of omega sub c
» Visualization 2: MP4 (3175 KB)      Simulated experiments
» Visualization 3: MP4 (7395 KB)      SNR 5db
» Visualization 4: MP4 (6916 KB)      SNR 15db
» Visualization 5: MP4 (6352 KB)      SNR 25db
» Visualization 6: MP4 (4747 KB)      Sequence1 stabilization results
» Visualization 7: MP4 (4527 KB)      Sequence 2 stabilization results
» Visualization 8: MP4 (3107 KB)      Sequence 3 stabilization results

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Figures (10)

Fig. 1
Fig. 1 Block diagram for the proposed video stabilization framework.
Fig. 2
Fig. 2 LRP and Local information (a) Input image, (b) LRP, (c) Local amplitude and local phase.
Fig. 3
Fig. 3 The high-frequency fluctuations of turbulence distortion. (a) One frame of Sequence 2. (b) Local amplitude and local phase temporal varying of the green point .
Fig. 4
Fig. 4 Latent sharp image used for simulation.
Fig. 5
Fig. 5 The effects of ωc (see Visualization 1) (a) one frame from the simulated video, (b) ωc = 0.1, (c) ωc = 0.3, (d) ωc = 0.5, (e) ωc = 0.8.
Fig. 6
Fig. 6 Simulated comparison results (see Visualization 2) (a) [12] output, (b) [11] output, (c) the proposed output (with ωc = 0.1).
Fig. 7
Fig. 7 Results of additional noises (see Visualization 3, Visualization 4, Visualization 5) (a) one frame of simulated video with noise, (b) one frame of [11] output, (c) one frame of [12] output, (d) one frame of the proposed output.
Fig. 8
Fig. 8 Sequence1 stabilization results (see Visualization 6) (a) one frame from sequence 1, (b) [12] output, (c) [11] output, (d) the proposed output.
Fig. 9
Fig. 9 Sequence 2 stabilization results (see Visualization 7) (a) one frame from sequence 2, (b) [12] output, (c) [11] output, (d) the proposed output.
Fig. 10
Fig. 10 Sequence 3 stabilization results (see Visualization 8) (a) one frame from sequence 3, (b) [12] output, (c) [11] output, (d) the proposed output.

Tables (4)

Tables Icon

Table 1 Average PSNR (dB) for differentωc values

Tables Icon

Table 2 Comparison of the Average PSNR (dB)

Tables Icon

Table 3 Performance with white Gaussian noise

Tables Icon

Table 4 Comparison of running time (s)

Equations (14)

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

g k ( i,j )=REDUCE( g k1 )= m=2 2 n=2 2 w( m,n ) g k1 ( 2i+m,2j+n )
g k,2 ( i,j )=EXPAND( g k )=4 m=2 2 n=2 2 w( m,n ) g k ( im 2 , jn 2 )
L { 1 } =IEXPAND( g 2 )=I g 2,2
L { k } = g k EXPAND( g k+1 )= g k g k+1,2 for 2k<N
L { N } = g N
g k1 = L { k1 } +EXPAND( g k )
I out = L { 1 } +EXPAND( g 2 )
( x )=( R 1 ( x ) R 2 ( x ) )=( h x I( x ) h y I( x ) )
I=Acos( φ ), R 1 =Asin( φ )cos( θ ), R 2 =Asin( φ )sin( θ )
A= I 2 + R 1 2 + R 2 2
φ= cos 1 ( I/A )
I Turbu ( x,y,t )=[ A( x,y )+Δ( x,y,t ) ]cos[ φ( x,y )+δ( x,y,t ) ]
H Butter ( ω ) 2 = 1 1+ ( ω/ ω c ) 2
k=0 N a[ k ]out[ nk ]= k=0 M b[ k ] input[ nk ]

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