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Article Dans Une Revue IEEE Transactions on Image Processing Année : 2014

Does deblurring improve geometrical hyperspectral unmixing?

Résumé

In this paper, we consider hyperspectral unmixing problems where the observed images are blurred during the acquisition process, \emph{e.g.} in microscopy and spectroscopy. We derive a joint observation and mixing model and show how it affects endmember identifiability within the geometrical unmixing framework. An analysis of the model reveals that nonnegative blurring results in a contraction of both the minimum-volume enclosing and maximum-volume enclosed simplex. We demonstrate this contraction property in the case of a spectrally-invariant point-spread function. The benefit of prior deconvolution on the accuracy of the restored sources and abundances is illustrated using simulated and real Raman spectroscopic data.
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Dates et versions

hal-00933013 , version 1 (30-01-2014)

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Simon Henrot, Charles Soussen, Manuel Dossot, David Brie. Does deblurring improve geometrical hyperspectral unmixing?. IEEE Transactions on Image Processing, 2014, 23 (3), pp.1169-1180. ⟨10.1109/TIP.2014.2300822⟩. ⟨hal-00933013⟩
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