Dr. Jérome Bobin Profile

Dr. Jérome Bobin

at Commissariat à l'Énergie Atomique

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Publications (7)

Proceedings Article | 26 September 2013 Paper

Sparsity and cosmology: inverse problems in cosmic microwave background experiments

F. Sureau, J. Bobin, J.-L. Starck

Proceedings Volume 8858, 885811 (2013) https://doi.org/10.1117/12.2024040

KEYWORDS: Spherical lenses, Data modeling, Microwave radiation, Wavelets, Inverse problems, Statistical analysis, Instrument modeling, Point spread functions, Synchrotrons, Thermal modeling

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Proceedings Article | 3 February 2012 Paper

Hyperspectral fluorescence microscopy based on compressed sensing

Vincent Studer, Jérome Bobin, Makhlad Chahid, Hamed Mousavi, Emmanuel Candes, Maxime Dahan

Proceedings Volume 8227, 82270D (2012) https://doi.org/10.1117/12.908532

KEYWORDS: Luminescence, Microscopy, Digital micromirror devices, Compressed sensing, Sensors, Microscopes, Hyperspectral imaging, Image compression, Cameras, Micromirrors

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Proceedings Article | 4 September 2009 Paper

Compressed sensing in astronomy and remote sensing: a data fusion perspective

J. Bobin, J.-L. Starck

Proceedings Volume 7446, 74460I (2009) https://doi.org/10.1117/12.830633

KEYWORDS: Astronomy, Compressed sensing, Wavelets, Raster graphics, Data fusion, Remote sensing, Data compression, Image compression, Earth sciences, Signal processing

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Proceedings Article | 4 September 2009 Paper

Sparsity constraints for hyperspectral data analysis: linear mixture model and beyond

J. Bobin, Y. Moudden, J.-L. Starck, J. Fadili

Proceedings Volume 7446, 74461D (2009) https://doi.org/10.1117/12.826131

KEYWORDS: Data modeling, Associative arrays, Convolution, Signal to noise ratio, Data analysis, Wavelets, Data processing, Matrices, Mars, Carbon dioxide

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Proceedings Article | 20 September 2007 Paper

Wavelets and curvelets on the sphere for polarized data

Y. Moudden, P. Abrial, J.-L. Starck, J. Bobin

Proceedings Volume 6701, 670116 (2007) https://doi.org/10.1117/12.734616

KEYWORDS: Transform theory, Optical spheres, Wavelets, Spherical lenses, Polarization, Wavelet transforms, Anisotropy, Algorithm development, Statistical analysis, Microwave radiation

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The statistics of the temperature anisotropies in the primordial Cosmic Microwave Background radiation field provide a wealth of information for cosmology and the estimation of cosmological parameters. An even more acute inference should stem from the study of maps of the polarization state of the CMB radiation. Measuring the latter extremely weak CMB polarization signal requires very sensitive instruments. The full-sky maps of both temperature and polarization anisotropies of the CMB to be delivered by the upcoming Planck Surveyor satellite experiment are hence awaited with excitement. Still, analyzing CMB data requires tackling a number of practical difficulties, notably that several other astrophysical sources emit radiation in the frequency range of CMB observations. Separating the different astrophysical foreground components and the CMB proper from available multichannel data is a problem that has drawn much attention in the community. Nevertheless, some level of residual contributions, most significantly in the galactic region and at the locations of strong radio point sources will unavoidably contaminate the estimated spherical CMB map. Masking out these regions is common practice but the gaps in the data need proper handling. In order to restore the stationarity of a partly incomplete CMB map and thus lower the impact of the gaps on non-local statistical tests, we developed an inpainting algorithm on the sphere to fill in the gaps, based on an iterative thresholding scheme in a sparse representation of the data. This algorithm relies on the variety of recently developed transforms on the sphere among which several multiscale transforms which we will review. We also contribute to enlarging the set of available transforms for polarized data on the sphere. We describe new multiscale decompositions namely the isotropic undecimated wavelet and curvelet transforms for polarized data on the sphere. The proposed transforms are invertible and so allow for applications in image restoration and denoising.

Proceedings Article | 20 September 2007 Paper

Morphological diversity and sparsity: new insights into multivariate data analysis

J. Bobin, J. Fadili, Y. Moudden, J.-L. Starck

Proceedings Volume 6701, 67011U (2007) https://doi.org/10.1117/12.731589

KEYWORDS: Signal to noise ratio, Associative arrays, Denoising, Chemical species, RGB color model, Image restoration, Signal processing, Inverse problems, Data modeling, Image processing

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Over the last few years, the development of multi-channel sensors motivated interest in methods for the coherent processing of multivariate data. From blind source separation (BSS) to multi/hyper-spectral data restoration, an extensive work has already been dedicated to multivariate data processing. Previous work has emphasized on the fundamental role played by sparsity and morphological diversity to enhance multichannel signal processing. Morphological diversity has been first introduced in the mono-channel case to deal with contour/texture extraction. The morphological diversity concept states that the data are the linear combination of several so-called morphological components which are sparse in different incoherent representations. In that setting, piecewise smooth features (contours) and oscillating components (textures) are separated based on their morphological differences assuming that contours (respectively textures) are sparse in the Curvelet representation (respectively Local Discrete Cosine representation). In the present paper, we define a multichannel-based framework for sparse multivariate data representation. We introduce an extension of morphological diversity to the multichannel case which boils down to assuming that each multichannel morphological component is diversely sparse spectrally and/or spatially. We propose the Generalized Morphological Component Analysis algorithm (GMCA) which aims at recovering the so-called multichannel morphological components. Hereafter, we apply the GMCA framework to two distinct multivariate inverse problems : blind source separation (BSS) and multichannel data restoration. In the two aforementioned applications, we show that GMCA provides new and essential insights into the use of morphological diversity and sparsity for multivariate data processing. Further details and numerical results in multivariate image and signal processing will be given illustrating the good performance of GMCA in those distinct applications.

Proceedings Article | 18 September 2005 Open Access

Morphological component analysis

J.-L. Starck, Y. Moudden, J. Bobin, M. Elad, D. Donoho

Proceedings Volume 5914, 59140Q (2005) https://doi.org/10.1117/12.615237

KEYWORDS: Associative arrays, Independent component analysis, Data modeling, Wavelets, Statistical analysis, Image compression, Feature extraction, Image processing, Image analysis, Image transmission

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