Simon Henrot

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The Linear Mixing Model is often used to perform Hyperspectral Unmixing because of its simplicity, but it assumes that a single spectral signature can be completely representative of an endmember. However, in many scenarios, this assumption does not hold since many factors such as illumination conditions and intrinsic variability of the endmembers have(More)
In this paper, we consider the problem of unmixing a time series of hyperspectral images. We propose a dynamical model based on linear mixing processes at each time instant. The spectral signatures and fractional abundances of the pure materials in the scene are seen as latent variables, and assumed to follow a general dynamical structure. Based on a(More)
In this paper, we consider hyperspectral unmixing problems where the observed images are blurred during the acquisition process, e.g., in microscopy and spectroscopy. We derive a joint observation and mixing model and show how it affects end-member identifiability within the geometrical unmixing framework. An analysis of the model reveals that nonnegative(More)
We consider a hyperspectral image restoration problem in which the solution is known to be nonnegative. The image estimate is obtained as the constrained minimizer of a convex criterion incorporating prior information on its spatial and spectral regularity. We previously proposed a fast algorithm for Tikhonov regularization. Here, we adapt this algorithm to(More)
This work aims at studying a method to automatically estimate regularization parameters of hyperspectral images deconvolution methods. The deconvolution problem is formulated as a multi-objective optimization problem and the properties of the corresponding response surface are studied. Based on these properties, the minimum distance criterion (MDC) is(More)
This paper aims at studying a method to automatically estimate the regularization parameters of non-negative hyperspectral image deconvolution methods. The deconvolution problem is formulated as a multi-objective optimization problem and the properties of the corresponding response surface are studied. Based on these properties, the minimum distance(More)
Fluorescence microscopy is a popular technique for multidimensional analysis of biological specimens. Within this framework, hyperspectral imaging allows to provide additional information on the sample of interest. However, the acquisition process induces various degradations on the image which can prevent quantitative post-processing treatment algorithms(More)