American Institute of Mathematical Sciences

Advanced
August  2014, 8(3): 733-760. doi: 10.3934/ipi.2014.8.733

Bayesian image restoration for mosaic active imaging

Nicolas Lermé 1, , François Malgouyres 2, , Dominique Hamoir 3, and  Emmanuelle Thouin 3,

1. 

LTCI, CNRS UMR5141, Institut Mines-Télécom, Télécom ParisTech, Paris, France
2. 

IMT, CNRS UMR5219, Université de Toulouse, Toulouse, France
3. 

ONERA - The French Aerospace Lab, F-31055 Toulouse, France, France

Received  December 2012 Revised  February 2014 Published  August 2014

In this paper, we focus on the restoration of images acquired with a new active imaging concept. This new instrument generates a mosaic of active imaging acquisitions. We first describe a simplified forward model of this so-called ``mosaic active imaging''. We also assume a prior on the distribution of images, using the (TV), and deduce a restoration algorithm. This algorithm is a two-stage iterative process which alternates between: i) the estimation of the restored image; ii) the estimation of the acquisition parameters. We then provide the details useful to the implementation of these two steps. In particular, we show that the image estimation can be performed with graph cuts. This allows a fast resolution of this image estimation step. Finally, we detail numerical experiments showing that acquisitions made with a mosaic active imaging device can be restored even under severe noise levels, with few acquisitions.
Keywords: graph cuts., laser imaging, image reconstruction, Active imaging, image estimation.
Mathematics Subject Classification: Primary: 68U10; Secondary: 94A08, 62M40, 90C27, 90C3.
Citation: Nicolas Lermé, François Malgouyres, Dominique Hamoir, Emmanuelle Thouin. Bayesian image restoration for mosaic active imaging. Inverse Problems & Imaging, 2014, 8 (3) : 733-760. doi: 10.3934/ipi.2014.8.733
References:
[1]

J. Bect, L. Blanc-Féraud, G. Aubert and A. Chambolle, A L1-unified variational framework for image restoration,, in Computer Vision - ECCV 2004, (2004), 1.   Google Scholar

[2]

D. Bertsekas, Nonlinear Programming,, 2nd edition, (2003).   Google Scholar

[3]

Y. Boykov and V. Kolmogorov, An experimental comparison of min-cut/max-flow algorithms for energy minimization in vision,, Pattern Analysis And Machine Intelligence, 26 (2004), 1124.  doi: 10.1109/TPAMI.2004.60.  Google Scholar

[4]

A. Chambolle, An algorithm for total variation minimization and applications,, Journal of Mathematical Imaging and Vision, 20 (2004), 89.   Google Scholar

[5]

A. Chambolle, Total variation minimization and a class of binary MRF models,, in Energy Minimization Methods in Computer Vision and Pattern Recognition, (3757), 136.  doi: 10.1007/11585978_10.  Google Scholar

[6]

A. Chambolle and J. Darbon, On total variation minimization and surface evolution using parametric maximum flows,, International Journal of Computer Vision, 84 (2009), 288.  doi: 10.1007/s11263-009-0238-9.  Google Scholar

[7]

A. Chambolle and T. Pock, A first-order primal-dual algorithm for convex problems with applications to imaging,, Journal of Mathematical Imaging and Vision, 40 (2011), 120.  doi: 10.1007/s10851-010-0251-1.  Google Scholar

[8]

T. Chan and J. Shen, Variational image inpainting,, Communications in Pure and Applied Math., 58 (2005), 579.  doi: 10.1002/cpa.20075.  Google Scholar

[9]

F. Chassat, Optical Propagation through Atmospheric Turbulence: Moral Study and Application of Anisoplanatism in Adaptive Optics,, PhD thesis, (1992).   Google Scholar

[10]

R. R. Coifman and A. Sowa, Combining the calculus of variations and wavelets for image enhancement,, Applied and Computational Harmonic Analysis, 9 (2000), 1.  doi: 10.1006/acha.2000.0299.  Google Scholar

[11]

J. Darbon and M. Sigelle, Image restoration with discrete constrained total variation. I. Fast and exact optimization,, Journal of Mathematical Imaging and Vision, 26 (2006), 261.  doi: 10.1007/s10851-006-8803-0.  Google Scholar

[12]

G. Demoment, Image reconstruction and restoration: Overview of common estimation structures and problems,, IEEE, 37 (1989), 2024.  doi: 10.1109/29.45551.  Google Scholar

[13]

M. Elad, Sparse and Redundant Representations: From Theory to Applications in Signal and Image Processing,, Springer, (2010).  doi: 10.1007/978-1-4419-7011-4.  Google Scholar

[14]

R. Fante, Electromagnetic beam propagation in turbulent media,, Proceedings of the IEEE, 63 (1975), 1669.  doi: 10.1109/PROC.1975.10035.  Google Scholar

[15]

D. Fried, Optical resolution through a randomly inhomogeneous medium for very long and very short exposures,, Journal of the Optical Society of America, 56 (1966), 1372.  doi: 10.1364/JOSA.56.001372.  Google Scholar

[16]

D. Greig, B. Porteous and A. Seheult, Exact maximum a posteriori estimation for binary images,, Journal of the Royal Statistical Society, 51 (1989), 271.   Google Scholar

[17]

D. Hamoir, Procédé et système d'imagerie active à champ large: Method and system for active imaging with a large field,, Patent WO 2010119225, (2010).   Google Scholar

[18]

L. Hespel, M.-T. Velluet, A. Bonnefois, N. Rivière, M. Fraces, D. Hamoir, B. Tanguy, B. Duchenne and J. Isbert, Comparison of a physics-based BIL simulator with experiments,, in International Symposium on Photoelectronic Detection and Imaging, (2009).   Google Scholar

[19]

J. Kiefer, Sequential minimax search for a maximum,, Proceedings of the American Mathematical Society, 4 (1953), 502.  doi: 10.1090/S0002-9939-1953-0055639-3.  Google Scholar

[20]

A. Kolmogorov, The local structure of turbulence in incompressible viscous fluid for very large Reynolds numbers,, Doklady Akademii Nauk SSSR, 66 (1949).   Google Scholar

[21]

V. Kolmogorov and R. Zabih, What energy functions can be minimized via graph cuts?,, Pattern Analysis And Machine Intelligence, 26 (2004), 147.  doi: 10.1109/TPAMI.2004.1262177.  Google Scholar

[22]

F. Luisier, T. Blu and M. Unser, Image denoising in mixed Poisson-Gaussian noise,, IEEE Transactions on Image Processing, 20 (2011), 696.  doi: 10.1109/TIP.2010.2073477.  Google Scholar

[23]

F. Malgouyres and F. Guichard, Edge direction preserving image zooming: A mathematical and numerical analysis,, SIAM Journal on Numerical Analysis, 39 (2001), 1.  doi: 10.1137/S0036142999362286.  Google Scholar

[24]

Y. Mao and J. Gilles, Non rigid geometric distortions correction - Application to atmospheric turbulence stabilization,, Inverse Problems and Imaging, 6 (2012), 531.  doi: 10.3934/ipi.2012.6.531.  Google Scholar

[25]

Y. Meyer, Oscillating Patterns in Image Processing and in Some Nonlinear Evolution Equations,, The Fifteenth Dean Jacqueline B. Lewis Memorial Lectures, (2001).   Google Scholar

[26]

M. Mäkitalo and A. Foi, Optimal inversion of the generalized Anscombe transformation for Poisson-Gaussian noise,, IEEE Transactions on Image Processing, 22 (2013), 91.  doi: 10.1109/TIP.2012.2202675.  Google Scholar

[27]

M. Nikolova, Local strong homogeneity of a regularized estimator,, SIAM Journal of Applied Mathematics, 61 (2000), 633.  doi: 10.1137/S0036139997327794.  Google Scholar

[28]

J. Picard and H. Ratliff, Minimum cuts and related problems,, Networks, 5 (1975), 357.  doi: 10.1002/net.3230050405.  Google Scholar

[29]

N. Rivière, L. Hespel, M.-T. Velluet, Y.-M. Frédéric, P. Barillot and F. Hélias, Modeling of an active burst illumination imaging system: Comparison between experimental and modelled 3d scene,, in Society of Photo-Optical Instrumentation Engineers, (7382), 783509.  doi: 10.1117/12.864694.  Google Scholar

[30]

F. Roddier, The effects of atmospheric turbulence in optical astronomy,, Progress in Optics, 19 (1981), 281.  doi: 10.1016/S0079-6638(08)70204-X.  Google Scholar

[31]

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

show all references

References:
[1]

J. Bect, L. Blanc-Féraud, G. Aubert and A. Chambolle, A L1-unified variational framework for image restoration,, in Computer Vision - ECCV 2004, (2004), 1.   Google Scholar

[2]

D. Bertsekas, Nonlinear Programming,, 2nd edition, (2003).   Google Scholar

[3]

Y. Boykov and V. Kolmogorov, An experimental comparison of min-cut/max-flow algorithms for energy minimization in vision,, Pattern Analysis And Machine Intelligence, 26 (2004), 1124.  doi: 10.1109/TPAMI.2004.60.  Google Scholar

[4]

A. Chambolle, An algorithm for total variation minimization and applications,, Journal of Mathematical Imaging and Vision, 20 (2004), 89.   Google Scholar

[5]

A. Chambolle, Total variation minimization and a class of binary MRF models,, in Energy Minimization Methods in Computer Vision and Pattern Recognition, (3757), 136.  doi: 10.1007/11585978_10.  Google Scholar

[6]

A. Chambolle and J. Darbon, On total variation minimization and surface evolution using parametric maximum flows,, International Journal of Computer Vision, 84 (2009), 288.  doi: 10.1007/s11263-009-0238-9.  Google Scholar

[7]

A. Chambolle and T. Pock, A first-order primal-dual algorithm for convex problems with applications to imaging,, Journal of Mathematical Imaging and Vision, 40 (2011), 120.  doi: 10.1007/s10851-010-0251-1.  Google Scholar

[8]

T. Chan and J. Shen, Variational image inpainting,, Communications in Pure and Applied Math., 58 (2005), 579.  doi: 10.1002/cpa.20075.  Google Scholar

[9]

F. Chassat, Optical Propagation through Atmospheric Turbulence: Moral Study and Application of Anisoplanatism in Adaptive Optics,, PhD thesis, (1992).   Google Scholar

[10]

R. R. Coifman and A. Sowa, Combining the calculus of variations and wavelets for image enhancement,, Applied and Computational Harmonic Analysis, 9 (2000), 1.  doi: 10.1006/acha.2000.0299.  Google Scholar

[11]

J. Darbon and M. Sigelle, Image restoration with discrete constrained total variation. I. Fast and exact optimization,, Journal of Mathematical Imaging and Vision, 26 (2006), 261.  doi: 10.1007/s10851-006-8803-0.  Google Scholar

[12]

G. Demoment, Image reconstruction and restoration: Overview of common estimation structures and problems,, IEEE, 37 (1989), 2024.  doi: 10.1109/29.45551.  Google Scholar

[13]

M. Elad, Sparse and Redundant Representations: From Theory to Applications in Signal and Image Processing,, Springer, (2010).  doi: 10.1007/978-1-4419-7011-4.  Google Scholar

[14]

R. Fante, Electromagnetic beam propagation in turbulent media,, Proceedings of the IEEE, 63 (1975), 1669.  doi: 10.1109/PROC.1975.10035.  Google Scholar

[15]

D. Fried, Optical resolution through a randomly inhomogeneous medium for very long and very short exposures,, Journal of the Optical Society of America, 56 (1966), 1372.  doi: 10.1364/JOSA.56.001372.  Google Scholar

[16]

D. Greig, B. Porteous and A. Seheult, Exact maximum a posteriori estimation for binary images,, Journal of the Royal Statistical Society, 51 (1989), 271.   Google Scholar

[17]

D. Hamoir, Procédé et système d'imagerie active à champ large: Method and system for active imaging with a large field,, Patent WO 2010119225, (2010).   Google Scholar

[18]

L. Hespel, M.-T. Velluet, A. Bonnefois, N. Rivière, M. Fraces, D. Hamoir, B. Tanguy, B. Duchenne and J. Isbert, Comparison of a physics-based BIL simulator with experiments,, in International Symposium on Photoelectronic Detection and Imaging, (2009).   Google Scholar

[19]

J. Kiefer, Sequential minimax search for a maximum,, Proceedings of the American Mathematical Society, 4 (1953), 502.  doi: 10.1090/S0002-9939-1953-0055639-3.  Google Scholar

[20]

A. Kolmogorov, The local structure of turbulence in incompressible viscous fluid for very large Reynolds numbers,, Doklady Akademii Nauk SSSR, 66 (1949).   Google Scholar

[21]

V. Kolmogorov and R. Zabih, What energy functions can be minimized via graph cuts?,, Pattern Analysis And Machine Intelligence, 26 (2004), 147.  doi: 10.1109/TPAMI.2004.1262177.  Google Scholar

[22]

F. Luisier, T. Blu and M. Unser, Image denoising in mixed Poisson-Gaussian noise,, IEEE Transactions on Image Processing, 20 (2011), 696.  doi: 10.1109/TIP.2010.2073477.  Google Scholar

[23]

F. Malgouyres and F. Guichard, Edge direction preserving image zooming: A mathematical and numerical analysis,, SIAM Journal on Numerical Analysis, 39 (2001), 1.  doi: 10.1137/S0036142999362286.  Google Scholar

[24]

Y. Mao and J. Gilles, Non rigid geometric distortions correction - Application to atmospheric turbulence stabilization,, Inverse Problems and Imaging, 6 (2012), 531.  doi: 10.3934/ipi.2012.6.531.  Google Scholar

[25]

Y. Meyer, Oscillating Patterns in Image Processing and in Some Nonlinear Evolution Equations,, The Fifteenth Dean Jacqueline B. Lewis Memorial Lectures, (2001).   Google Scholar

[26]

M. Mäkitalo and A. Foi, Optimal inversion of the generalized Anscombe transformation for Poisson-Gaussian noise,, IEEE Transactions on Image Processing, 22 (2013), 91.  doi: 10.1109/TIP.2012.2202675.  Google Scholar

[27]

M. Nikolova, Local strong homogeneity of a regularized estimator,, SIAM Journal of Applied Mathematics, 61 (2000), 633.  doi: 10.1137/S0036139997327794.  Google Scholar

[28]

J. Picard and H. Ratliff, Minimum cuts and related problems,, Networks, 5 (1975), 357.  doi: 10.1002/net.3230050405.  Google Scholar

[29]

N. Rivière, L. Hespel, M.-T. Velluet, Y.-M. Frédéric, P. Barillot and F. Hélias, Modeling of an active burst illumination imaging system: Comparison between experimental and modelled 3d scene,, in Society of Photo-Optical Instrumentation Engineers, (7382), 783509.  doi: 10.1117/12.864694.  Google Scholar

[30]

F. Roddier, The effects of atmospheric turbulence in optical astronomy,, Progress in Optics, 19 (1981), 281.  doi: 10.1016/S0079-6638(08)70204-X.  Google Scholar

[31]

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

[1]

Simon Hubmer, Andreas Neubauer, Ronny Ramlau, Henning U. Voss. On the parameter estimation problem of magnetic resonance advection imaging. Inverse Problems & Imaging, 2018, 12 (1) : 175-204. doi: 10.3934/ipi.2018007
[2]

Lacramioara Grecu, Constantin Popa. Constrained SART algorithm for inverse problems in image reconstruction. Inverse Problems & Imaging, 2013, 7 (1) : 199-216. doi: 10.3934/ipi.2013.7.199
[3]

Wenxiang Cong, Ge Wang, Qingsong Yang, Jia Li, Jiang Hsieh, Rongjie Lai. CT image reconstruction on a low dimensional manifold. Inverse Problems & Imaging, 2019, 13 (3) : 449-460. doi: 10.3934/ipi.2019022
[4]

Jianjun Zhang, Yunyi Hu, James G. Nagy. A scaled gradient method for digital tomographic image reconstruction. Inverse Problems & Imaging, 2018, 12 (1) : 239-259. doi: 10.3934/ipi.2018010
[5]

Habib Ammari, Josselin Garnier, Vincent Jugnon. Detection, reconstruction, and characterization algorithms from noisy data in multistatic wave imaging. Discrete & Continuous Dynamical Systems - S, 2015, 8 (3) : 389-417. doi: 10.3934/dcdss.2015.8.389
[6]

Chengxiang Wang, Li Zeng, Yumeng Guo, Lingli Zhang. Wavelet tight frame and prior image-based image reconstruction from limited-angle projection data. Inverse Problems & Imaging, 2017, 11 (6) : 917-948. doi: 10.3934/ipi.2017043
[7]

Yunhai Xiao, Junfeng Yang, Xiaoming Yuan. Alternating algorithms for total variation image reconstruction from random projections. Inverse Problems & Imaging, 2012, 6 (3) : 547-563. doi: 10.3934/ipi.2012.6.547
[8]

Ming Yan, Alex A. T. Bui, Jason Cong, Luminita A. Vese. General convergent expectation maximization (EM)-type algorithms for image reconstruction. Inverse Problems & Imaging, 2013, 7 (3) : 1007-1029. doi: 10.3934/ipi.2013.7.1007
[9]

Florian Bossmann, Jianwei Ma. Enhanced image approximation using shifted rank-1 reconstruction. Inverse Problems & Imaging, 2020, 14 (2) : 267-290. doi: 10.3934/ipi.2020012
[10]

Kaitlyn Muller. The relationship between backprojection and best linear unbiased estimation in synthetic-aperture radar imaging. Inverse Problems & Imaging, 2016, 10 (2) : 549-561. doi: 10.3934/ipi.2016011
[11]

Thomas März, Andreas Weinmann. Model-based reconstruction for magnetic particle imaging in 2D and 3D. Inverse Problems & Imaging, 2016, 10 (4) : 1087-1110. doi: 10.3934/ipi.2016033
[12]

Daniela Calvetti, Erkki Somersalo. Microlocal sequential regularization in imaging. Inverse Problems & Imaging, 2007, 1 (1) : 1-11. doi: 10.3934/ipi.2007.1.1
[13]

Guillaume Bal, Olivier Pinaud, Lenya Ryzhik. On the stability of some imaging functionals. Inverse Problems & Imaging, 2016, 10 (3) : 585-616. doi: 10.3934/ipi.2016013
[14]

Yonggui Zhu, Yuying Shi, Bin Zhang, Xinyan Yu. Weighted-average alternating minimization method for magnetic resonance image reconstruction based on compressive sensing. Inverse Problems & Imaging, 2014, 8 (3) : 925-937. doi: 10.3934/ipi.2014.8.925
[15]

Yunmei Chen, Xiaojing Ye, Feng Huang. A novel method and fast algorithm for MR image reconstruction with significantly under-sampled data. Inverse Problems & Imaging, 2010, 4 (2) : 223-240. doi: 10.3934/ipi.2010.4.223
[16]

Shenglong Hu, Zheng-Hai Huang, Hong-Yan Ni, Liqun Qi. Positive definiteness of Diffusion Kurtosis Imaging. Inverse Problems & Imaging, 2012, 6 (1) : 57-75. doi: 10.3934/ipi.2012.6.57
[17]

Peijun Li, Yuliang Wang. Near-field imaging of obstacles. Inverse Problems & Imaging, 2015, 9 (1) : 189-210. doi: 10.3934/ipi.2015.9.189
[18]

Kaitlyn (Voccola) Muller. SAR correlation imaging and anisotropic scattering. Inverse Problems & Imaging, 2018, 12 (3) : 697-731. doi: 10.3934/ipi.2018030
[19]

Josselin Garnier. Ghost imaging in the random paraxial regime. Inverse Problems & Imaging, 2016, 10 (2) : 409-432. doi: 10.3934/ipi.2016006
[20]

Liliana Borcea, Dinh-Liem Nguyen. Imaging with electromagnetic waves in terminating waveguides. Inverse Problems & Imaging, 2016, 10 (4) : 915-941. doi: 10.3934/ipi.2016027

2018 Impact Factor: 1.469

Tools

Metrics

  • PDF downloads (37)
  • HTML views (0)
  • Cited by (0)

Other articles
by authors

[Back to Top]

Copyright © 2020 American Institute of Mathematical Sciences