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Déconvolution et séparation d'images hyperspectrales en microscopie

Abstract : Hyperspectral imaging refers to the acquisition of spatial images at enough spectral bands to produce highly resolved pixel spectra. When applied to microscopy, this technique allows to collect additional information about the specimens of interest. Processing hyperspectral data is often challenging due to the blurring caused by the observation system, mathematically expressed as a convolution. The operation of deconvolution is thus necessary to restore the original image from the observations. Image restoration falls into the class of inverse problems, as opposed to the direct problem which consists in modeling the image degradation process, treated in part 1 of the thesis. Another inverse problem with many applications in hyperspectral imaging consists in extracting the pure materials making up the image, called endmembers, and their fractional contribution to the data or abundances. This problem is termed spectral unmixing and its resolution accounts for the nonnegativity of the endmembers and abundances. Part 2 presents algorithms designed to efficiently solve the hyperspectral image restoration problem, formulated as the minimization of a composite criterion. The methods are based on a common framework allowing to account for several a priori assumptions on the solution, including a nonnegativity constraint and the preservation of edges in the image. The performance of the proposed algorithms are demonstrated on fluorescence confocal images of bacterial biosensors. Part 3 deals with the spectral unmixing problem from a geometrical viewpoint. A sufficient condition on abundance coefficients for the identifiability of endmembers is proposed. We derive and study a joint observation model and mixing model and demonstrate the interest of performing deconvolution as a prior step to spectral unmixing on confocal Raman microscopy data.
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Submitted on : Wednesday, January 15, 2014 - 2:50:36 PM
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  • HAL Id : tel-00931579, version 1

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Simon Henrot. Déconvolution et séparation d'images hyperspectrales en microscopie. Traitement du signal et de l'image [eess.SP]. Université de Lorraine, 2013. Français. ⟨tel-00931579⟩

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