Nelly Pustelnik






CNRS
CNRS

Journals

  1. B. Pascal, N. Pustelnik, and P. Abry,
    Nonsmooth convex joint estimation of local regularity and local variance for fractal texture segmentation,
    submitted, 2019.


  2. Y. Kaloga, M. Foare, N. Pustelnik, and P. Jensen,
    Discrete Mumford-Shah on graph for mixing matrix estimation,
    accepted to IEEE Signal Processing Letters, 2019.
  3. [Toolbox MATLAB]

  4. M. Foare, N. Pustelnik, and L. Condat,
    Semi-linearized proximal alternating minimization for a discrete Mumford-Shah model,
    submitted, 2018.
  5. [PDF] [Toolbox MATLAB]

  6. J. Colas, N. Pustelnik, C. Oliver, J.-C. Géminard, V. Vidal,
    Nonlinear denoising for solid friction dynamics characterization,
    submitted, 2018.


  7. M. Jiu, N. Pustelnik, S. Janaqi, M. Chebre, P. Ricoux,
    Sparse hierarchical interaction learning with epigraphical projection,
    Submitted, 2018.
  8. [PDF]

  9. J. Boulanger, N. Pustelnik, L. Condat, T. Piolot, L. Sengmanivong,
    Nonsmooth convex optimization for Structured Illumination Microscopy image reconstruction,
    Inverse problems, vol. 34, no. 9, 22pp., July 2018.
  10. [PDF]

  11. P. Abry, J. Spilka, R. Leonarduzzi, V. Chudácek, N. Pustelnik, M. Doret,
    Sparse learning for Intrapartum fetal heart rate analysis,
    Biomedical Physics & Engineering Express, vol. 4, no. 3, 034002, 2018.


  12. N. Pustelnik, L. Condat,
    Proximity Operator of a Sum of Functions; Application to Depth Map Estimation,
    IEEE Signal Processing Letters,
    vol. 24, no. 12, pp. 1827 - 1831, Dec. 2017.
  13. [PDF] [Toolbox MATLAB]

  14. J. Frecon, N. Pustelnik, N. Dobigeon, H. Wendt, and P. Abry,
    Bayesian selection for the l2-Potts model regularization parameter: 1D piecewise constant signal denoising,
    IEEE Trans. on Signal Processing,
    vol. 65, no. 25, pp. 5215 - 5224, Jun. 2017.
  15. [PDF][Toolbox MATLAB]

  16. J. Spilka, J. Frecon, R. Leonarduzzi, N. Pustelnik, P. Abry, M. Doret,
    Sparse Support Vector Machine for Intrapartum Fetal Heart Rate Classification,
    IEEE Journal of Biomedical and Health Informatics,
    vol. 21, no. 3, pp. 664 - 671, May 2017.
  17. [PDF][Toolbox MATLAB]

  18. G. Michau, N. Pustelnik, P. Borgnat, P. Abry, A. Nantes, A. Bhaskar, E. Chung,
    A primal-dual algorithm for link dependent origin destination matrix estimation,
    IEEE Transactions on Signal and Information Processing over Networks,
    vol. 3, no. 1, pp. 104 - 113, Mar. 2017.
  19. [PDF]

  20. N. Pustelnik, H. Wendt, P. Abry, N. Dobigeon,
    Combining Local Regularity Estimation and Total Variation Optimization for Scale-Free Texture Segmentation,
    IEEE Trans. on Computational Imaging,
    vol. 2, no. 4, pp. 468 - 479, Dec. 2016.
  21. [PDF]

  22. J. Frecon, G. Didier, N. Pustelnik, and P. Abry,
    Non-Linear Wavelet Regression and Branch and Bound Optimization for the Full Identification of Bivariate Operator Fractional Brownian Motion,
    IEEE Trans. on Signal Processing,
    vol. 64, no. 15, pp. 4040 - 4049, Aug. 2016.
  23. [PDF] [Toolbox MATLAB]

  24. G. Chierchia, N. Pustelnik, J.-C. Pesquet, B. Pesquet-Popescu,
    A Proximal Approach for Sparse Multiclass SVM,
    Rapport, 2016.
  25. [PDF]

  26. J. Frecon, N. Pustelnik, P. Abry, and L. Condat,
    On-The-Fly Approximation of Multivariate Total Variation Minimization,
    IEEE Trans. on Signal Processing,
    vol. 64, no. 9, pp. 2355 - 2364, May 2016.
  27. [PDF] [Toolbox MATLAB]

  28. N. Pustelnik, A. Benazza-Benhayia, Y. Zheng, J.-C. Pesquet,
    Wavelet-based Image Deconvolution and Reconstruction,
    Wiley Encyclopedia of Electrical and Electronics Engineering,
    DOI: 10.1002/047134608X.W8294, Feb. 2016. (Tutorial paper)
  29. [PDF]

  30. G. Chierchia, N. Pustelnik, J.-C. Pesquet, B. Pesquet-Popescu,
    Epigraphical splitting for solving constrained convex formulations of inverse problems with proximal tools,
    Signal, Image and Video Processing,
    vol.9, no. 8, pp.1737--1749, Nov. 2015.
  31. [PDF] [Toolbox MATLAB]

  32. G. Chierchia, N. Pustelnik, B. Pesquet-Popescu, J.-C. Pesquet,
    A Non-Local Structure Tensor Based Approach for Multicomponent Image Recovery Problems,
    IEEE Trans. Image processing,
    vol. 23, no. 12, pp. 5233--5248, Oct. 2014.
  33. [PDF] [Toolbox MATLAB]

  34. J. Schmitt, N. Pustelnik, P. Borgnat, P. Flandrin, and L. Condat,
    A 2-D Prony-Huang Transform: A New Tool for 2-D Spectral Analysis,
    IEEE Trans. Image processing,
    vol. 23, no. 12, pp. 5531--5544, Oct. 2014.
  35. [PDF] [Toolbox MATLAB]

  36. C.R. Johnson, P. Messier, W.A. Sethares, A.G. Klein, C. Brown, A.H. Do, P. Klausmeyer, P. Abry, S. Jaffard, H. Wendt, S. Roux, N. Pustelnik, N. van Noord, L. van der Maaten, E. Potsma, J. Coddington, L.A. Daffner, H. Murata, H. Wilhelm, S. Wood, M. Messier,
    Pursuing automated classification of historic photographic papers from raking light photomicrographs,
    Journal of the American Institute for Conservation, vol. 53, no. 3, pp. 159-170, 2014.


  37. N. Pustelnik, P. Borgnat, P. Flandrin
    Empirical Mode Decomposition revisited by multicomponent non smooth convex optimization,
    Signal Processing, Vol. 102, pp. 313--331, Sept. 2014.
  38. [PDF][Bib] [Toolbox MATLAB]

  39. Y. Berthoumieu, C. Dossal, N. Pustelnik, P. Ricoux, and F. Turcu
    An evaluation of the sparsity degree for sparse recovery with deterministic measurement matrices,
    Journal of Mathematical Imaging and Vision, Vol. 48, pp. 266--278, 2013.
  40. [PDF][Bib]

  41. J.-C. Pesquet and N. Pustelnik,
    A Parallel Inertial Proximal Optimization Method,
    Pacific Journal of Optimization, Vol. 8, No. 2, pp. 273--305, Apr. 2012.
  42. [PDF] [Bib]

  43. N. Pustelnik, J.-C. Pesquet, and C. Chaux,
    Relaxing Tight Frame Condition in Parallel Proximal Methods for Signal Restoration,
    IEEE Signal Processing Letters, Vol. 60, No. 2, pp. 968--973, Feb. 2012.
  44. [PDF] [Bib]

  45. L. Chaari, E. Chouzenoux, N. Pustelnik, C. Chaux et S. Moussaoui,
    OPTIMED : Optimisation itérative pour la résolution de problèmes inverses de grande taille,
    Traitement du signal, Vol. 28, No. 3-4, pp. 329-374, 2011.
  46. [PDF] [Bib]

  47. N. Pustelnik, C. Chaux, and J.-C. Pesquet,
    Parallel ProXimal Algorithm for image restoration using hybrid regularization,
    IEEE Transactions on Image Processing, Vol. 20, No. 9, pp. 2450-2462, Sep. 2011.
  48. [PDF][Bib] [Toolbox MATLAB]

  49. L. M. Briceño-Arias, P. L. Combettes, J.-C. Pesquet, and N. Pustelnik,
    Proximal algorithms for multicomponent image processing,
    Journal of Mathematical Imaging and Vision, Vol. 41, No. 1, pp. 3-22, Sep. 2011.
  50. [PDF][Bib]

  51. C. Chaux, J.-C. Pesquet, and N. Pustelnik,
    Nested iterative algorithms for convex constrained image recovery problems,
    SIAM Journal on Imaging Sciences, Vol. 2, No. 2, pp. 730-762, Jun. 2009.
  52. [PDF] [Bib] [Toolbox MATLAB]



Workshops / Conferences



  1. N. Pustelnik, L. Condat,
    Proximity operator of a sum of functions: Application to image segmentation,
    SIAM Conference on Imaging Science, Bologna, Italy June, 5-8 2018.
  2. [No paper]

  3. B. Pascal, N. Pustelnik, P. Abry,
    Combining Local Regularity Estimation and Total Variation Optimization for Scale-Free Texture Segmentation,
    SIAM Conference on Imaging Science, Bologna, Italy June, 5-8 2018.
  4. [No paper]

  5. M. Foare, N. Pustelnik, L. Condat,
    Semi-Linearized Proximal Alternating Minimization for a Discrete Mumford-Shah Model,
    SIAM Conference on Imaging Science, Bologna, Italy June, 5-8 2018.
  6. [No paper]

  7. B. Pascal, N. Pustelnik, P. Abry, M. Serres, V. Vidal
    Joint estimation of local variance and local regularity for texture segmentation. Application to multiphase flow characterization
    ,
    IEEE ICIP, Athens, Greece, Oct. 7-10, 2018.
  8. [PDF]

  9. M. Foare, N. Pustelnik, L. Condat
    A new proximal method for joint image restoration and edge detection with the Mumford-Shah model
    ,
    IEEE ICASSP, Calgary, Alberta, Canada, Apr. 15-20, 2018.
  10. [PDF]

  11. B. Pascal, N. Pustelnik, P. Abry, J.-C. Pesquet
    Block-coordinate proximal algorithms for scale-free texture segmentation
    ,
    IEEE ICASSP, Calgary, Alberta, Canada, Apr. 15-20, 2018.
  12. [PDF]

  13. J. Frecon, N. Pustelnik, N. Dobigeon, H. Wendt, and P. Abry
    Sélection du paramètre de régularisation dans le problème l2-Potts
    ,
    GRETSI, Juan-les-Pins, France, Sep. 5-8, 2017.


  14. J. Frecon, N. Pustelnik, N. Dobigeon, H. Wendt, and P. Abry
    Bayesian-driven criterion to automatically select the regularization parameter in the l1-Potts model
    ,
    IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP), New-Orleans, USA, May, 5-9 2017.


  15. G. Chierchia, N. Pustelnik, J.-C. Pesquet
    Random primal-dual proximal iterations for sparse multiclass SVM
    ,
    IEEE Machine Learning for Signal Processing (MLSP) workshop, Vietri sul Mare, Salerno, Italy, Sep. 13-16, 2016.


  16. M. Jiu, N. Pustelnik, M. Chebre, S. Janaqi, P. Ricoux
    Multiclass SVM with graph path coding regularization for face classification
    ,
    IEEE Machine Learning for Signal Processing (MLSP) workshop, Vietri sul Mare, Salerno, Italy, Sep. 13-16, 2016.


  17. J. Frecon, N. Pustelnik, H. Wendt, L. Condat, and P. Abry
    Multifractal-based texture segmentation using variational procedure
    ,
    IEEE Image Video and Multidimensional Signal Processing (IVMSP) workshop, Bordeaux, France, Jul., 11-12 2016.


  18. J. Frecon, R. Fontugne, G. Didier, N. Pustelnik, K. Fukuda, and P. Abry
    Non-linear regression for bivariate self-similarity identification - Application to anomaly detection in Internet traffic based on a joint scaling analysis of packet and byte counts
    ,
    IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP), Shanghai, China, Mar, 20-25 2016.


  19. P. Flandrin, N. Pustelnik, P. Borgnat,
    On Wigner-based sparse time-frequency distributions
    ,
    IEEE CAMSAP, Cancun, Mexico, Dec. 13-16 2015.


  20. J. Frecon, N. Pustelnik, H. Wendt, and P. Abry
    Multivariate optimization for multifractal-based texture segmentation
    ,
    IEEE International Conference in Image Processing (ICIP), Quebec City, Canada, Sept, 27-30 2015.


  21. L. Condat, N. Pustelnik
    Segmentation d'image par optimisation proximale,
    GRETSI, Lyon, France, September 8-11, 2015.


  22. J. Frecon, N. Pustelnik, H. Wendt, and P. Abry,
    Variation totale multivariée pour la détection de changement du spectre multifractal,
    GRETSI, Lyon, France, September 8-11, 2015.


  23. G. Michau, P. Borgnat, N. Pustelnik, P. Abry, A. Nantes, E. Chung
    Estimating link-dependent origin-destination matrices from sample trajectories and traffic counts,
    GRETSI, Lyon, France, September 8-11, 2015.


  24. J. Schmitt, E. Horne, N. Pustelnik, S. Joubaud, P. Odier
    An improved variational mode decomposition method for internal waves separation
    ,
    European Signal Processing Conference (EUSIPCO), Nice, France, Aug. 31- Sep. 4 2015.


  25. J. Spilka, J. Frecon, R.F. Leonarduzzi, N. Pustelnik, P. Abry, M. Doret
    Intrapartum Fetal Heart Rate classification from Trajectory in Sparse SVM feature space
    ,
    37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Milan, Italy, Aug. 25-29 2015.


  26. R.F. Leonarduzzi, J. Spilka, J. Frecon, H. Wendt, N. Pustelnik, S. Jaffard, P. Abry, M. Doret
    P-leader multifractal analysis and sparse SVM for intrapartum fetal acidosis detection
    ,
    37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Milan, Italy, Aug. 25-29 2015.


  27. G. Chierchia, N. Pustelnik, J.-C. Pesquet, and B. Pesquet-Popescu,
    An Epigraphic Splitting Technique for Sparse Multiclass SVM,
    Signal Processing with Adaptive Sparse Structured Representations (SPARS), Cambridge, UK, July 6-9, 2015.


  28. G. Michau, P. Borgnat, N. Pustelnik, P. Abry, A. Nantes, and E. Chung,
    Estimating link-dependen origin-destination matrices from sample trajectories and traffic counts
    ,
    IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP), Brisbane, Australia, Apr, 19-24 2015.


  29. J. Frecon, N. Pustelnik, N. Dobigeon, H. Wendt, and P. Abry,
    Hybrid Bayesian variational scheme to handle parameter selection in total variation signal denoising
    ,
    European Signal Processing Conference (EUSIPCO), Lisbon, Portugal, Sept, 1-5 2014.


  30. N. Pustelnik, P. Abry, H. Wendt, and N. Dobigeon,
    Inverse problem formulation for regularity estimation in images,
    IEEE International Conference in Image Processing (ICIP), La Défense, Paris, France, October 27-30, 2014.
  31. (Top 10% papers)

  32. J. Schmitt, N. Pustelnik, P. Borgnat, and P. Flandrin,
    2D Hilbert-Huang Transform,
    IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP), Florence, Italy, May 4-9, 2014
  33. [PDF] [Bib]

  34. G. Chierchia, N. Pustelnik, J.-C. Pesquet, and B. Pesquet-Popescu,
    Epigraphic proximal projection for sparse Multiclass SVM,
    IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP), Florence, Italy, May 4-9, 2014
  35. [PDF] [Bib]

  36. J. Boulanger, N. Pustelnik, L. Condat,
    Non-smooth convex optimization for an efficient reconstruction in structured illumination microscopy,
    IEEE International Symposium on Biomedical Imaging (ISBI), Beijing, China, April 28-May 2, 2014.
  37. [PDF] [Bib]

  38. L. Condat, J. Boulanger, N. Pustelnik, S. Sahnoun, L. Sengmanivong
    A 2-D spectral analysis method to estimate the modulation parameters in structured illumination microscopy,
    IEEE International Symposium on Biomedical Imaging (ISBI), Beijing, China, April 28-May 2, 2014.
  39. [PDF] [Bib]

  40. N. Saulig, N. Pustelnik, P. Borgnat, P. Flandrin, and V. Sucic,
    Instantaneous counting of components in nonstationary signals,
    European Signal Processing Conference (EUSIPCO), Marrakech, Morocco, Sept. 9-13, 2013.
  41. (Invited paper)
    [PDF] [Bib]

  42. N. Pustelnik, H. Wendt, and P. Abry,
    Régularité locale pour l'analyse de texture : le mariage des coefficients dominants et de la minimisation proximale,
    GRETSI, Brest, France, September 3-6, 2013.
  43. [PDF] [Bib]

  44. N. Pustelnik, H. Wendt, and P. Abry,
    Local regularity for texture segmentation : combining wavelet leaders and proximal minimization,
    IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP), Vancouver, Canada, May 26-31, 2013
  45. [PDF] [Bib]

  46. G. Chierchia, N. Pustelnik, J.-C. Pesquet, and B. Pesquet-Popescu,
    An epigraphical convex optimization approach for multicomponent image restoration using non-local structure tensor,
    IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP), Vancouver, Canada, May 26-31, 2013
  47. [PDF] [Bib]

  48. N. Pustelnik, P. Borgnat, and P. Flandrin,
    A multicomponent proximal algorithm for Empirical Mode Decomposition,
    European Signal Processing Conference (EUSIPCO), Bucharest, Romania, August, 27-31, 2012.
  49. [PDF] [Bib]

  50. N. Pustelnik, C. Dossal, F. Turcu, Y. Berthoumieu, and Ph. Ricoux,
    A greedy algorithm to extract sparsity degree for l1/l0-equivalence in a deterministic context,
    European Signal Processing Conference (EUSIPCO), Bucharest, Romania, August, 27-31, 2012.
  51. [PDF] [Bib]

  52. G. Chierchia, N. Pustelnik, J.-C. Pesquet, and B. Pesquet-Popescu,
    A proximal approach for constrained cosparse modelling,
    IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP), Kyoto, Japan, March 25-30, 2012.
  53. [PDF] [Bib]

  54. N. Pustelnik, F. Turcu, C. Dossal, Y. Berthoumieu, and Ph. Ricoux,
    On l1/l0-equivalence in a deterministic context. Application to limited view angle tomography,
    Mathematics and Image Analysis (MIA'12), Paris, France, January 16-18 2012.
  55. [No paper]

  56. N. Pustelnik, J.-C. Pesquet, and C. Chaux,
    Bancs de filtres et méthodes proximales pour la restauration d'images,
    GRETSI, Bordeaux, France, September 5-8, 2011.
  57. [PDF] [Bib]

  58. C. Chaux, C. Comtat, J.-C. Pesquet, and N. Pustelnik,
    Dynamic PET Reconstruction using Parallel ProXimal Algorithm,
    SIAM Conference on Optimization, Darmstadt, Germany, May 16-19 2011.
  59. [No paper]

  60. N. Pustelnik, C. Chaux, J.-C. Pesquet, and C. Comtat,
    Parallel Algorithm and Hybrid Regularization for Dynamic PET Reconstruction,
    IEEE Medical Imaging Conference , Knoxville, Tennessee, Oct. 30 - Nov. 6 2010.
  61. [PDF] [Bib]

  62. L. M. Briceño-Arias, P. L. Combettes, J.-C. Pesquet, and N. Pustelnik,
    Proximal method for geometry and texture image decomposition,
    IEEE International Conference on Image Processing (ICIP) , Honk Kong, 26-29 Septembre 2010.
  63. [PDF] [Bib]

  64. N. Pustelnik, J.-C. Pesquet, and C. Chaux,
    Proximal methods for image restoration using a class of non-tight frame representations
    ,
    European Signal Processing Conference (EUSIPCO), Aalborg, Danmark, 23-27 Août 2010.
  65. [PDF] [Bib]

  66. C. Chaux, J.-C. Pesquet, and N. Pustelnik,
    Frame-based proximal algorithms for Poisson data recovery,
    SIAM Conference on Imaging Science, Chicago, Illinois, April 12-14 2010.
  67. [No paper]

  68. N. Pustelnik, C. Chaux, and J.-C. Pesquet,
    Extension des algorithmes imbriqués pour la résolution de problèmes d'optimisation convexe en imagerie,
    GRETSI, Dijon, France, September 8-11, 2009.
  69. [PDF] [Bib]

  70. N. Pustelnik, C. Chaux, J.-C. Pesquet, F. C. Sureau, E. Dusch, and C. Comtat,
    Adapted Convex Optimization Algorithm for Wavelet-Based Dynamic PET Reconstruction,
    International Meeting on Fully Three-Dimensional Image Reconstruction in Radiology and Nuclear Medicine (Fully3D) , Beijing, China, September 5-10, 2009.
  71. [PDF] [Bib]

  72. N. Pustelnik, C. Chaux, and J.-C. Pesquet,
    Hybrid regularization for data restoration in the presence of Poisson noise,
    European Signal Processing Conference (EUSIPCO), Glasgow, Scotland, August 24-28, 2009.
  73. (Invited paper)
    [PDF] [Bib]


  74. L. Chaari, N. Pustelnik, C. Chaux, and J.-C. Pesquet,
    Solving inverse problems with overcomplete transforms and convex optimization techniques,
    SPIE, San Diego, California, USA , August 2-6, 2009.
  75. (Invited paper)
    [PDF] [Bib]


  76. N. Pustelnik, C. Chaux, and J.-C. Pesquet,
    A wavelet-based quadratic extension method for image deconvolution in the presence of Poisson noise,
    IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP), Taipei, Taiwan , April 19-24, 2009.
  77. [PDF] [Bib]

  78. N. Pustelnik, C. Chaux, and J.-C. Pesquet,
    A constrained forward-backward algorithm for image recovery problems,
    European Signal Processing Conference (EUSIPCO), Lausanne, Switzerland, August 25-29, 2008.
  79. [PDF] [Bib]




PhD

Proximal methods for the resolution of inverse problems. Application to Positron Emission Tomography. [HAL]

The objective of this work is to propose reliable, efficient and fast methods for minimizing convex criteria, that are found in inverse problems for imagery. We focus on restoration/reconstruction problems when data is degraded with both a linear operator and noise, where the latter is not assumed to be necessarily additive.
The methods reliability is ensured through the use of proximal algorithms, the convergence of which is guaranteed when a convex criterion is considered. Efficiency is sought through the choice of criteria adapted to the noise characteristics, the linear operators and the image specificities. Of particular interest are regularization terms based on total variation and/or sparsity of signal frame coefficients. As a consequence of the use of frames, two approaches are investigated, depending on whether the analysis or the synthesis formulation is chosen. Fast processing requirements lead us to consider proximal algorithms with a parallel structure.
Theoretical results are illustrated on several large inverse problems arising in image restoration, stereoscopy, multi-spectral imagery and decomposition into texture and geometry components. We focus on a particular application, namely Positron Emission Tomography (PET), which is particularly difficult because of the presence of a projection operator combined with Poisson noise, leading to highly corrupted data. To optimize the quality of the reconstruction, we make use the spatio-temporal characteristics of brain tissue activity.