In the pipeline
A. Blelly, H. Moutarde, J.BobinSparse data inpainting for the recovery of Galactic Binaries gravitational wave signals from gapped data, in press, 2021.
R. Carloni Gertosio, J. Bobin, F. Acero, Semi-Blind Source Separation with Learned Physic-Driven Constraints, submitted, 2021.
J. Bobin, R. Carloni Gertosio, C. Bobin and C. Thiam, Non-linear interpolation learning for example-based inverse problem regularization, submitted, 2021.
J.Xu, J.Bobin, A. De Vismes, C.Bobin, Quantitative analysis of gamma-ray spectra with spectral unmixing: calibrations for HPGe detectors, in press, 2021.
J.Xu, J.Bobin, A. De Vismes, C.Bobin, Analysis of gamma-ray spectra with spectral unmixing: determination of characteristic limits, in press, 2021.
A. Picquenot, F. Acero, T. Holland-Ashford, L. A. Lopez, J. Bobin, Three dimensional morphological asymmetries in the ejecta of Cassiopeia A using a component separation method in X-rays, submitted to A&A, 2020.
Sparse matrix factorization and blind source separation
R.Carloni Gertosio, J.Bobin, Joint deconvolution and unsupervised source separation for data on the sphere, Signal Processing, in press, 2021.
J.Bobin, I. El Hamzaoui, F.Acero, A.Picquenot, Sparse BSS from Poisson measurements, IEEE Tr. on Image Processing, accepted, 2020.
C.Kervazo, J.Bobin, C.Chenot, F.Sureau, Use of PALM for l_1 Sparse Matrix Factorization: Difficulty and Rationalization of a Two-Step Approach, DSP, vol.97, 2020.
J.Bobin , J. Rapin, J.L. Starck and A. Larue, Sparsity and adaptivity for the blind separation of partially correlated sources, IEEE TSP, 63(5), 2015.
Y.Moudden and J.Bobin, Hyperspectral BSS using GMCA with spatio-spectral sparsity constraints – IEEE Transactions on image processing – Vol 20. Issue 3. pages 872-879 (2011).
J.Bobin, J.-L. Starck, J. Fadili, Y.Moudden, Sparsity and Morphological Diversity in Blind Source Separation, IEEE Transactions on Image Processing, Vol.16, N.11, p. 2662-2674, November 2007.
Sparse Non-Negative Matrix Factorization (NMF)
C.Chenot, J.Bobin, Blind Source Separation with outliers in transformed domains, SIAM Imag. Sciences, vol.11, issue 2, 2018.
C.Chenot, J.Bobin, Unsupervised separation of sparse sources in the presence of outliers, Signal Processing, vol. 138, 2017.
BSS in the large-scale regime
C. Kervazo, J.Bobin, C.Chenot, Blind separation of a large number of sparse sources, Signal Processing, vol. 150, 2018.
Distributed sparse unsupervised matrix factorization
C. Kervazo, T. Liaudat, J.Bobin, Faster and better sparse BSS through mini-batch optimization, accepted to DSP, 2020.
Sparsity and signal processing
S.R. Becker, J.Bobin and E. Candes, Nesta: a fast and accurate first-order method for compressed sensing – 2009 – SIAM Journal of Imaging Science, Vol 4 #11 (2011).
J.-L. Starck and J.Bobin, Astronomical Data Analysis and Sparsity: from Wavelets to Compressed Sensing – 2009 – proceedings of the IEEE, special issue – Vol 98 – Issue 5 (2010).
Applications in cosmology and astrophysics
GRAVITATIONAL WAVES WITH LISA
A.Blelly, J.Bobin, H.Moutarde, Sparsity Based Recovery of Galactic Binaries Gravitational Waves, accepted, Phys. Rev. D, 2020.
Cosmological microwave background
J.Bobin, F.Sureau, J-L Starck, CMB estimation from the WMAP and Planck PR2 data, A&A, 2016.
J.Bobin, F.Sureau, J.-L. Starck, Polarized CMB map recovery with sparse component separation, A&A, 2015.
A.Rassat, J-L Starck, P. Paykari, F. Sureau and J.Bobin, Planck CMB anomalies: astrophysical and cosmological foregrounds and the curse of masking, A&A, 2014.
J.Bobin, J.-L. Starck, F. Sureau and S. Basak, Sparse component separation for accurate CMB map estimation, A&A, 550, K.2013.
M. Irfan, J.Bobin, M-A Miville-Deschênes, I. Grenier Determining thermal dust emission from Planck HFI data using a sparse, parametric technique, A&A, 623, A21, 2019.
M. Irfan, J.Bobin, Sparse estimation of model-based diffuse thermal dust emission, MNRAS, vol. 474, issue 4, 2018.
S.Cunnington, M. Irfan, I. Carucci, A. Pourtsidou, J.Bobin, 21cm foregrounds and polarization leakage: cleaning and mitigation strategies, MNRAS, accepted, 2021.
I.Carucci, M.Irfan, J.Bobin, GMCA foreground cleaning for 21cm IM experiments, recovery of 21 cm intensity maps with sparse component separation, MNRAS accepted, 2020.
M. Jiang, J.Bobin, J-L Starck, Joint Multichannel Deconvolution and Blind Source Separation, SIAM Imaging Science, 10(4), 2017.
E.Chapman, A.Bonaldi, G.Harker, V.Jelic, F.Abdalla, G.Bernardi, J.Bobin, F.Dulwich, B.Mort, M.Santos and J-L.Starck Cosmic dawn and Epoch of Reionization foreground removal with the SKA, accepted to the SKA science book ‘Advancing astrophysics with the Square Kilometre Array’, 2015.
E. Chapman, F. Abdalla, J.Bobin, J.L. Starck, G. Harker, V. Jelic, P. Labropoulos, S. Zaroubi, M. Brentjens, A. De Bruyn and L. Koopmanx, The scale of the problem: recovering images of reionization with GMCA, MNRAS, 459, 2013
A.Pujol, J.Bobin, F.Sureau, A.Guinot, M.Kilbinger, Shear measurement bias II: a fast machine learning calibration method, A&A, accepted, 2020.
A. Pujol, F. Sureau, J.Bobin, M. Gentile, F. Courbin, M.Kilbinger, Shear measurement bias. I: dependencies on methods, simulation parameters and measured parameters, A&A, accepted, 2020.
A. Pujol, M.Kilbinger, F. Sureau, J.Bobin, A highly precise shape-noise-free shear bias estimator, A&A, 621, A2, 2018.
A new look at X-ray multispectral images in astrophysics
A. Picquenot, F. Acero, J. Bobin, P. Maggi, J. Ballet, G.W. Pratt, A novel method for component separation of extended sources in X-ray astronomy, A&A 627, A139, 2019.
Applications of machine learning in astrophysics
M. Frontera-Pons, B. Moraes, F.Sureau, J.Bobin, F.Abdalla, Representation learning for automated spectroscopic redshift estimation, A&A, 625, 2019.
M. Frontera-Pons, F.Sureau, J.Bobin, E Le Floc’h, Unsupervised feature learning for galaxy SEDs with denoising autoencoders, A&A, 603, A60, 2017.
Applications in bio-medical signal processing
Applications in nuclear physics
R.André, C.Bobin, J.Bobin, J.Xu, A.De Vismes, Metrological approach of γ-emitting radionuclides identification at low statistics: application of sparse spectral unmixing to scintillation detectors, in press, Metrologia, 2020.
C.Bobin, H.Paradis, J.Bobin, J.Bouchard, V.Lourenço, C.Thiam, R.André, L.Ferreux, A. de Vismes Ott, M. Thevenin, Spectral Unmixing Applied To Fast identification of gamma-emitting radionuclides using NaI(Tl) detectors, Applied Radiations and Isotopes, in press, 2020.
Applications in optical signal processing
J.Fade, E. Perrotin, J.Bobin, Two-pixel polarimetric camera by compressive sensing, Applied Optics, vol. 57, issue 7, 2017.
V. Studer, J. Bobin, M. Chahid, H. Mousavi, E. Candes, and M. Dahan, Compressive fluorescence microscopy for biological and hyperspectral imaging, PNAS 2012 109 (26).
Book chapter :
J.Bobin, J.-L. Starck, Y.Moudden, J. Fadili, Blind Source Separation: the Sparsity Revolution, Advances in Imaging and
Electron Physics, Vol. 152, p. 221-298 – Peter W. Hawkes Ed. – 2008.
Technical report :
S.Ben Hadj, J.Bobin, A. Woiselle, Local subspace projection-based anomaly detection for complex multi-spectral images, Technical report – CEA Saclay, 2015.
R.Lguensat, J. Bobin, F.Sureau, Non-linear optimization under sparsity constraint, Technical report – CEA Saclay, September 2014.
Q.Leone, E. LeFloch, J.Bobin, A new method for galaxy classification, an application to cluster detection, Technical report – CEA Saclay, July 2014.
J. Bobin, Y. Zheng, Multichannel data analysis via sparse/low rank matrix decomposition, Technical report – CEA Saclay, June 2011.
J. Bobin, J.-L. Starck, Compressed Sensing for Hershel, Technical report – CEA Saclay, September 2006.
PhD dissertation (in French):
HDR dissertation (in French):