American Institute of Mathematical Sciences

Advanced
August  2012, 6(3): 531-546. doi: 10.3934/ipi.2012.6.531

Non rigid geometric distortions correction - Application to atmospheric turbulence stabilization

Yu Mao 1, and  Jérôme Gilles 2,

1. 

Institute for Mathematics and Its Applications, University of Minnesota, 425 Lind Hall 207 Church Street SE, Minneapolis, MN 55455-0134, United States
2. 

Department of Mathematics, University of California Los Angeles, 520 Portola Plaza, Los Angeles, CA 90095-1555, United States

Received  July 2011 Revised  January 2012 Published  September 2012

A novel approach is presented to recover an image degraded by atmospheric turbulence. Given a sequence of frames affected by turbulence, we construct a variational model to characterize the static image. The optimization problem is solved by Bregman Iteration and the operator splitting method. Our algorithm is simple, efficient, and can be easily generalized for different scenarios.
Keywords: optical flow, Bregman iterations., Turbulence restoration, nonlocal total variation.
Mathematics Subject Classification: Primary: 58F15, 58F17; Secondary: 53C3.
Citation: Yu Mao, Jérôme Gilles. Non rigid geometric distortions correction - Application to atmospheric turbulence stabilization. Inverse Problems and Imaging, 2012, 6 (3) : 531-546. doi: 10.3934/ipi.2012.6.531
References:
[1]

M. S. C. Almeida and L. B. Almeida, Blind and semi-blind deblurring of natural images, IEEE Transactions on Image Processing, 19 (2010), 36-52. doi: 10.1109/TIP.2009.2031231.

[2]

M. Aubailly, M. A. Vorontsov, G. W. Carhat and M. T. Valley, "Automated Video Enhancement from a Stream of Atmospherically-Distorted Images: the Lucky-Region Fusion Approach," Proceedings of SPIE, 7463 (2009).

[3]

M. J. Black and P. Anandan, The robust estimation of multiple motions: Parametric and piecewise-smooth flow fields, Comput Vision and Image Understanding, 63 (1996), 75-104. doi: 10.1006/cviu.1996.0006.

[4]

J. Y. Bouguet, "Pyramidal Implementation of the Lucas Kanade Feature Tracker Description of the Algorithm," Intel Corporation Microprocessor Research Labs, 2000. Available from: http://robots.stanford.edu/cs223b04/algo_tracking.pdf.

[5]

A. Buades, B. Coll and J. M. Morel, A review of image denoising algorithms, with a new one, Multiscale Model Sim, 4 (2005), 490-530.

[6]

J. F. Cai, S. Osher and Z. Shen, Linearized Bregman iterations for frame-based image deblurring, SIAM Journal on Imaging Sciences, 2 (2009), 226-252.

[7]

J. F. Cai, S. Osher and Z. Shen, Split Bregman methods and frame based image restoration, Multiscale Modeling and Simulation, 8 (2009), 337-369.

[8]

T. F. Chan and C. K. Wong, Convergence of the alternating minimization algorithm for blind deconvolution, Linear Algebra Appl., 316 (2000), 259-285. doi: 10.1016/S0024-3795(00)00141-5.

[9]

T. F. Chan, A. M. Yip and F. E. Park, Simultaneous total variation image inpainting and blind deconvolution, International Journal of Imaging Systems and Technology, 15 (2005), 92-102. doi: 10.1002/ima.20041.

[10]

P. L. Combettes and V. R. Wajs, Signal recovery by proximal forward-backward splitting, Multiscale Model Sim., 4 (2005), 1168-1200.

[11]

D. Frakes, J. Monaco and M. Smith, "Suppression of Atmospheric Turbulence in Video Using an Adaptive Control Grid Interpolation Approach," Proceedings of the IEEE International Conference on Acoustics, Speech, and Signal Processing, 2001.

[12]

S. Gepshtein, A. Shteinman and B. Fishbain, "Restoration of Atmospheric Turbulent Video Containing Real Motion Using Rank Filtering and Elastic Image Registration," Proceedings of the Eusipco, 2004.

[13]

G. Gilboa and S. Osher, Nonlocal operators with applications to image processing, Multiscale Model Sim., 7 (2008), 1005-1028.

[14]

J. Gilles, T. Dagobert and C. De Franchis, Atmospheric turbulence restoration by diffeomorphic image registration and blind deconvolution, in "Proceedings of Advanced Concepts for Intelligent Vision Systems,'' 2008.

[15]

D. Goldfarb and W. Yin, Parametric maximum flow algorithms for fast total variation minimization, SIAM J. Sci. Comput., 31 (2009), 3712-3743. doi: 10.1137/070706318.

[16]

T. Goldstein and S. Osher, The split Bregman method for L1 regularized problems, SIAM Journal on Imaging Sciences, 2 (2009), 323-343.

[17]

L. He, A. Marquina and S. Osher, Blind deconvolution using TV regularization and Bregman iteration, International Journal of Imaging Systems and Technology, 15 (2005), 74-83. doi: 10.1002/ima.20040.

[18]

M. Hirsch, S. Sra, B. Scholkopf and S. Harmeling, "Efficient Filter Flow for Space-variant Multiframe Blind Deconvolution," Computer Vision and Pattern Recognition Conference, 2010.

[19]

M. Lemaitre, "Etude de la Turbulence Atmosphérique en Vision Horizontale Lointaine et Restauration de Séquences Dégradées Dans le Visible et L'infrarouge,'' Ph. D Thesis, Université de Bourgogne, 2007.

[20]

D. Li, R. M. Mersereau and S. Simske, Atmospheric turbulence-degraded image restoration using principal components analysis, IEEE Geoscience and Remote Sensing Letters, 4 (2007), 340-344.

[21]

P. Lions and B. Mercier, Splitting algorithms for the sum of two nonlinear operators, SIAM Journal on Numerical Analysis, 16 (1979), 964-979. doi: 10.1137/0716071.

[22]

Y. Mao, B. P. Fahimian, S. Osher and J. Miao, Development and optimization of regularized tomographic reconstruction algorithms utilizing equally-sloped tomography, IEEE Transactions on Image Processing, 19 (2010), 1259-1268. doi: 10.1109/TIP.2009.2039660.

[23]

S. Osher, M. Burger, D. Goldfarb, J. Xu and W. Yin, An iterative regularization method for total variation-based image restoration, Multiscale Model Sim., 4 (2005), 460-489.

[24]

G. B. Passty, Ergodic convergence to a zero of the sum of monotone operators in Hilbert space, J. Math. Anal. Appl., 72 (1979), 383-390. doi: 10.1016/0022-247X(79)90234-8.

[25]

L. I. Rudin, S. Osher and E. Fatemi, Nonlinear total variation based noise removal algorithms, Physica D: Nonlinear Phenomena, 60 (1992), 259-268. doi: 10.1016/0167-2789(92)90242-F.

[26]

D. Sun, S. Roth, J. Lewis and M. J. Black, "Learning Optical Flow," Computer Vision-ECCV, 2008.

[27]

M. Tahtali, A. Lambert and D. Fraser, "Self-tuning Kalman Filter Estimation of Atmospheric Warp," Proceedings of SPIE, 2008.

[28]

X. Zhang, M. Burger, X. Bresson and S. Osher, Bregmanized nonlocal regularization for deconvolution and sparse reconstruction, SIAM Journal on Imaging Sciences, 3 (2010), 253-276.

[29]

X. Zhang, M. Burger and S. Osher, A unified primal-dual algorithm framework based on Bregman iteration, Journal of Scientific Computing, 46 (2010), 1-27.

[30]

X. Zhu and P. Milanfar, "Image Reconstruction from Videos Distorted by Atmospheric Turbulence," SPIE Electronic Imaging, Conference 7543 on Visual Information Processing and Communication, 2010.

show all references

References:
[1]

M. S. C. Almeida and L. B. Almeida, Blind and semi-blind deblurring of natural images, IEEE Transactions on Image Processing, 19 (2010), 36-52. doi: 10.1109/TIP.2009.2031231.

[2]

M. Aubailly, M. A. Vorontsov, G. W. Carhat and M. T. Valley, "Automated Video Enhancement from a Stream of Atmospherically-Distorted Images: the Lucky-Region Fusion Approach," Proceedings of SPIE, 7463 (2009).

[3]

M. J. Black and P. Anandan, The robust estimation of multiple motions: Parametric and piecewise-smooth flow fields, Comput Vision and Image Understanding, 63 (1996), 75-104. doi: 10.1006/cviu.1996.0006.

[4]

J. Y. Bouguet, "Pyramidal Implementation of the Lucas Kanade Feature Tracker Description of the Algorithm," Intel Corporation Microprocessor Research Labs, 2000. Available from: http://robots.stanford.edu/cs223b04/algo_tracking.pdf.

[5]

A. Buades, B. Coll and J. M. Morel, A review of image denoising algorithms, with a new one, Multiscale Model Sim, 4 (2005), 490-530.

[6]

J. F. Cai, S. Osher and Z. Shen, Linearized Bregman iterations for frame-based image deblurring, SIAM Journal on Imaging Sciences, 2 (2009), 226-252.

[7]

J. F. Cai, S. Osher and Z. Shen, Split Bregman methods and frame based image restoration, Multiscale Modeling and Simulation, 8 (2009), 337-369.

[8]

T. F. Chan and C. K. Wong, Convergence of the alternating minimization algorithm for blind deconvolution, Linear Algebra Appl., 316 (2000), 259-285. doi: 10.1016/S0024-3795(00)00141-5.

[9]

T. F. Chan, A. M. Yip and F. E. Park, Simultaneous total variation image inpainting and blind deconvolution, International Journal of Imaging Systems and Technology, 15 (2005), 92-102. doi: 10.1002/ima.20041.

[10]

P. L. Combettes and V. R. Wajs, Signal recovery by proximal forward-backward splitting, Multiscale Model Sim., 4 (2005), 1168-1200.

[11]

D. Frakes, J. Monaco and M. Smith, "Suppression of Atmospheric Turbulence in Video Using an Adaptive Control Grid Interpolation Approach," Proceedings of the IEEE International Conference on Acoustics, Speech, and Signal Processing, 2001.

[12]

S. Gepshtein, A. Shteinman and B. Fishbain, "Restoration of Atmospheric Turbulent Video Containing Real Motion Using Rank Filtering and Elastic Image Registration," Proceedings of the Eusipco, 2004.

[13]

G. Gilboa and S. Osher, Nonlocal operators with applications to image processing, Multiscale Model Sim., 7 (2008), 1005-1028.

[14]

J. Gilles, T. Dagobert and C. De Franchis, Atmospheric turbulence restoration by diffeomorphic image registration and blind deconvolution, in "Proceedings of Advanced Concepts for Intelligent Vision Systems,'' 2008.

[15]

D. Goldfarb and W. Yin, Parametric maximum flow algorithms for fast total variation minimization, SIAM J. Sci. Comput., 31 (2009), 3712-3743. doi: 10.1137/070706318.

[16]

T. Goldstein and S. Osher, The split Bregman method for L1 regularized problems, SIAM Journal on Imaging Sciences, 2 (2009), 323-343.

[17]

L. He, A. Marquina and S. Osher, Blind deconvolution using TV regularization and Bregman iteration, International Journal of Imaging Systems and Technology, 15 (2005), 74-83. doi: 10.1002/ima.20040.

[18]

M. Hirsch, S. Sra, B. Scholkopf and S. Harmeling, "Efficient Filter Flow for Space-variant Multiframe Blind Deconvolution," Computer Vision and Pattern Recognition Conference, 2010.

[19]

M. Lemaitre, "Etude de la Turbulence Atmosphérique en Vision Horizontale Lointaine et Restauration de Séquences Dégradées Dans le Visible et L'infrarouge,'' Ph. D Thesis, Université de Bourgogne, 2007.

[20]

D. Li, R. M. Mersereau and S. Simske, Atmospheric turbulence-degraded image restoration using principal components analysis, IEEE Geoscience and Remote Sensing Letters, 4 (2007), 340-344.

[21]

P. Lions and B. Mercier, Splitting algorithms for the sum of two nonlinear operators, SIAM Journal on Numerical Analysis, 16 (1979), 964-979. doi: 10.1137/0716071.

[22]

Y. Mao, B. P. Fahimian, S. Osher and J. Miao, Development and optimization of regularized tomographic reconstruction algorithms utilizing equally-sloped tomography, IEEE Transactions on Image Processing, 19 (2010), 1259-1268. doi: 10.1109/TIP.2009.2039660.

[23]

S. Osher, M. Burger, D. Goldfarb, J. Xu and W. Yin, An iterative regularization method for total variation-based image restoration, Multiscale Model Sim., 4 (2005), 460-489.

[24]

G. B. Passty, Ergodic convergence to a zero of the sum of monotone operators in Hilbert space, J. Math. Anal. Appl., 72 (1979), 383-390. doi: 10.1016/0022-247X(79)90234-8.

[25]

L. I. Rudin, S. Osher and E. Fatemi, Nonlinear total variation based noise removal algorithms, Physica D: Nonlinear Phenomena, 60 (1992), 259-268. doi: 10.1016/0167-2789(92)90242-F.

[26]

D. Sun, S. Roth, J. Lewis and M. J. Black, "Learning Optical Flow," Computer Vision-ECCV, 2008.

[27]

M. Tahtali, A. Lambert and D. Fraser, "Self-tuning Kalman Filter Estimation of Atmospheric Warp," Proceedings of SPIE, 2008.

[28]

X. Zhang, M. Burger, X. Bresson and S. Osher, Bregmanized nonlocal regularization for deconvolution and sparse reconstruction, SIAM Journal on Imaging Sciences, 3 (2010), 253-276.

[29]

X. Zhang, M. Burger and S. Osher, A unified primal-dual algorithm framework based on Bregman iteration, Journal of Scientific Computing, 46 (2010), 1-27.

[30]

X. Zhu and P. Milanfar, "Image Reconstruction from Videos Distorted by Atmospheric Turbulence," SPIE Electronic Imaging, Conference 7543 on Visual Information Processing and Communication, 2010.

[1]

Xiaoqun Zhang, Tony F. Chan. Wavelet inpainting by nonlocal total variation. Inverse Problems and Imaging, 2010, 4 (1) : 191-210. doi: 10.3934/ipi.2010.4.191
[2]

Chunlin Wu, Juyong Zhang, Xue-Cheng Tai. Augmented Lagrangian method for total variation restoration with non-quadratic fidelity. Inverse Problems and Imaging, 2011, 5 (1) : 237-261. doi: 10.3934/ipi.2011.5.237
[3]

Adriana González, Laurent Jacques, Christophe De Vleeschouwer, Philippe Antoine. Compressive optical deflectometric tomography: A constrained total-variation minimization approach. Inverse Problems and Imaging, 2014, 8 (2) : 421-457. doi: 10.3934/ipi.2014.8.421
[4]

Haijuan Hu, Jacques Froment, Baoyan Wang, Xiequan Fan. Spatial-Frequency domain nonlocal total variation for image denoising. Inverse Problems and Imaging, 2020, 14 (6) : 1157-1184. doi: 10.3934/ipi.2020059
[5]

Ke Chen, Yiqiu Dong, Michael Hintermüller. A nonlinear multigrid solver with line Gauss-Seidel-semismooth-Newton smoother for the Fenchel pre-dual in total variation based image restoration. Inverse Problems and Imaging, 2011, 5 (2) : 323-339. doi: 10.3934/ipi.2011.5.323
[6]

Rinaldo M. Colombo, Francesca Monti. Solutions with large total variation to nonconservative hyperbolic systems. Communications on Pure and Applied Analysis, 2010, 9 (1) : 47-60. doi: 10.3934/cpaa.2010.9.47
[7]

Lukas F. Lang, Otmar Scherzer. Optical flow on evolving sphere-like surfaces. Inverse Problems and Imaging, 2017, 11 (2) : 305-338. doi: 10.3934/ipi.2017015
[8]

Thomas Schuster, Joachim Weickert. On the application of projection methods for computing optical flow fields. Inverse Problems and Imaging, 2007, 1 (4) : 673-690. doi: 10.3934/ipi.2007.1.673
[9]

Aniello Raffaele Patrone, Otmar Scherzer. On a spatial-temporal decomposition of optical flow. Inverse Problems and Imaging, 2017, 11 (4) : 761-781. doi: 10.3934/ipi.2017036
[10]

Xianchao Xiu, Ying Yang, Wanquan Liu, Lingchen Kong, Meijuan Shang. An improved total variation regularized RPCA for moving object detection with dynamic background. Journal of Industrial and Management Optimization, 2020, 16 (4) : 1685-1698. doi: 10.3934/jimo.2019024
[11]

Yunho Kim, Paul M. Thompson, Luminita A. Vese. HARDI data denoising using vectorial total variation and logarithmic barrier. Inverse Problems and Imaging, 2010, 4 (2) : 273-310. doi: 10.3934/ipi.2010.4.273
[12]

Mujibur Rahman Chowdhury, Jun Zhang, Jing Qin, Yifei Lou. Poisson image denoising based on fractional-order total variation. Inverse Problems and Imaging, 2020, 14 (1) : 77-96. doi: 10.3934/ipi.2019064
[13]

Sören Bartels, Marijo Milicevic. Iterative finite element solution of a constrained total variation regularized model problem. Discrete and Continuous Dynamical Systems - S, 2017, 10 (6) : 1207-1232. doi: 10.3934/dcdss.2017066
[14]

J. Mead. $ \chi^2 $ test for total variation regularization parameter selection. Inverse Problems and Imaging, 2020, 14 (3) : 401-421. doi: 10.3934/ipi.2020019
[15]

Wei Wang, Ling Pi, Michael K. Ng. Saturation-Value Total Variation model for chromatic aberration correction. Inverse Problems and Imaging, 2020, 14 (4) : 733-755. doi: 10.3934/ipi.2020034
[16]

Yunhai Xiao, Junfeng Yang, Xiaoming Yuan. Alternating algorithms for total variation image reconstruction from random projections. Inverse Problems and Imaging, 2012, 6 (3) : 547-563. doi: 10.3934/ipi.2012.6.547
[17]

Juan C. Moreno, V. B. Surya Prasath, João C. Neves. Color image processing by vectorial total variation with gradient channels coupling. Inverse Problems and Imaging, 2016, 10 (2) : 461-497. doi: 10.3934/ipi.2016008
[18]

Liyan Ma, Lionel Moisan, Jian Yu, Tieyong Zeng. A stable method solving the total variation dictionary model with $L^\infty$ constraints. Inverse Problems and Imaging, 2014, 8 (2) : 507-535. doi: 10.3934/ipi.2014.8.507
[19]

Lu Liu, Zhi-Feng Pang, Yuping Duan. Retinex based on exponent-type total variation scheme. Inverse Problems and Imaging, 2018, 12 (5) : 1199-1217. doi: 10.3934/ipi.2018050
[20]

Leyu Hu, Wenxing Zhang, Xingju Cai, Deren Han. A parallel operator splitting algorithm for solving constrained total-variation retinex. Inverse Problems and Imaging, 2020, 14 (6) : 1135-1156. doi: 10.3934/ipi.2020058

2021 Impact Factor: 1.483

Tools

Metrics

  • PDF downloads (339)
  • HTML views (0)
  • Cited by (24)

Other articles
by authors

[Back to Top]

Copyright © 2022 American Institute of Mathematical Sciences | Privacy Policy