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This paper addresses blind-source separation in the case where both the source signals and the mixing coefficients are non-negative. The problem is referred to as non-negative source separation and the main application concerns the analysis of spectrometric data sets. The separation is performed in a Bayesian framework by encoding non-negativity through the(More)
In this paper we propose a joint estimation of the parameters and hyperparameters (the parameters of the prior law) when a Bayesian approach with Maximum Entropy (ME) priors is used to solve the inverse problems which arise in signal and image reconstruction and restoration problems. In particular we propose two methods: one based on the Expectation(More)
In this paper we propose a Bayesian framework for unsupervised image fusion and joint segmentation. More specifically we consider the case where we have observed images of the same object through different imaging processes or through different spectral bands (multi or hyper spectral images). The objective of this work is then to propose a coherent approach(More)
This paper considers the problem of source separation in the case of noisy instantaneous mixtures. In a previous work [1], sources have been modeled by a mixture of Gaussians leading to an hierarchical Bayesian model by considering the labels of the mixture as i.i.d hidden variables. We extend this modeliza-tion to incorporate a Markovian structure for the(More)
The paper considers the problem of source separation in the particular case where both the sources and the mixing coefficients are positive. The proposed method addresses the problem in a Bayesian framework. We assume a gamma distribution for the spectra and the mixing coefficients. This prior distribution enforces the non-negativity. This leads to an(More)
Acoustic source imaging has nowadays been widely used in source localization and separation. In this paper, based on the deconvolution methods (DAMAS), we propose a robust super-resolution approach with sparsity constraint (SC-RDAMAS) to estimate both the positions and powers of the sources, as well as the noise variance in low Signal to Noise Ratio (SNR)(More)
In this paper we present an application of Bayesian non-negative source separation to the analysis of spectral mixtures obtained from the analysis of multicomponent substances. The processing aims are formalized as a non-negative source separation problem. The proposed Bayesian inference for the analysis is introduced and the main steps of the estimation(More)
Blind source separation (BSS) has become one of the major signal and image processing area in many applications. Principal component analysis (PCA) and Independent component analysis (ICA) have become two main classical approaches for this problem. However, these two approaches have their limits which are mainly, the assumptions that the data are temporally(More)
To handle with inverse problems, two probabilistic approaches have been proposed: the maximum entropy on the mean (MEM) and the Bayesian estimation (BAYES). The main object of this presentation is to compare these two approaches which are in fact two diierent inference procedures to deene the solution of an inverse problem as the optimizer of a compound(More)