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- Quentin Barthelemy, Anthony Larue, Aurélien Mayoue, David Mercier, Jérôme I. Mars
- IEEE Transactions on Signal Processing
- 2012

Classical dictionary learning algorithms (DLA) allow unicomponent signals to be processed. Due to our interest in two-dimensional (2D) motion signals, we wanted to mix the two components to provide rotation invariance. So, multicomponent frameworks are examined here. In contrast to the well-known multichannel framework, a multivariate framework is first… (More)

- Quentin Barthelemy, Anthony Larue, Jérôme I. Mars
- IEEE Signal Processing Letters
- 2014

In this letter, a review of the quaternionic least mean squares (QLMS) algorithm is proposed. Three versions coming from three derivation ways exist: the original QLMS [1] based on componentwise gradients, HR-QLMS [2] based on a quaternion gradient operator and iQLMS [3] based on an involutions-gradient. Noting and investigating the differences between the… (More)

- Quentin Barthelemy, Anthony Larue, Aurélien Mayoue, Daniel Mercier, Jérôme I. Mars
- 2011 IEEE Statistical Signal Processing Workshop…
- 2011

In this article, we present a new tool for sparse coding : Multivariate DLA which empirically learns the characteristic patterns associated to a multivariate signals set. Once learned, Multivariate OMP approximates sparsely any signal of this considered set. These methods are specified to the 2D rotation-invariant case. Shift and rotation-invariant cases… (More)

- Anthony Larue, Dinh Tuan Pham
- 2006 14th European Signal Processing Conference
- 2006

We propose a frequency blind deconvolution algorithm based on mutual information rate as a measure of whiteness. In the case of seismic data, the algorithm of Wiggins [11] based on kurtosis, which is a supergaussianity criterion, is often used. We study the robustness in noisy context of these two algorithms, and compare them with Wiener filtering. We… (More)

- Jérémy Rapin, Jérôme Bobin, Anthony Larue, Jean-Luc Starck
- IEEE Transactions on Signal Processing
- 2013

Non-negative blind source separation (BSS) has raised interest in various fields of research, as testified by the wide literature on the topic of non-negative matrix factorization (NMF). In this context, it is fundamental that the sources to be estimated present some diversity in order to be efficiently retrieved. Sparsity is known to enhance such contrast… (More)

- Jérôme Bobin, Jérémy Rapin, Anthony Larue, Jean-Luc Starck
- IEEE Transactions on Signal Processing
- 2015

Blind source separation (BSS) is a very popular technique to analyze multichannel data. In this context, the data are modeled as the linear combination of sources to be retrieved. For that purpose, standard BSS methods all rely on some discrimination principle, whether it is statistical independence or morphological diversity, to distinguish between the… (More)

- Anthony Larue, Jérôme I. Mars, Christian Jutten
- IEEE Transactions on Signal Processing
- 2006

In this paper, a new blind single-input single-output (SISO) deconvolution method based on the minimization of the mutual information rate of the deconvolved output is proposed. The method works in the frequency domain and requires estimation of the signal probability density function. Thus, the algorithm uses higher order statistics (except for Gaussian… (More)

- Jérémy Rapin, Jérôme Bobin, Anthony Larue, Jean-Luc Starck
- SIAM J. Imaging Sciences
- 2014

Non-negative blind source separation (non-negative BSS), which is also referred to as non-negative matrix factorization (NMF), is a very active field in domains as different as astrophysics, audio processing or biomedical signal processing. In this context, the efficient retrieval of the sources requires the use of signal priors such as sparsity. If NMF has… (More)

In this work, we study Non-Negative Matrix Factorization (NMF) and compare standard algorithms with an extension to NMF of a Blind Source Separation algorithm using sparsity, Generalized Morphological Component Analysis (GMCA). We also develop a more robust version of GMCA handling more precisely the priors through sub-iterations, which we call rGMCA. We… (More)

- Quentin Barthelemy, Anthony Larue, Jérôme I. Mars
- Signal Processing
- 2014

A new model for describing a three-dimensional (3D) trajectory is proposed in this article. The studied trajectory is viewed as a linear combination of rotatable 3D patterns. The resulting model is thus 3D rotation invariant (3DRI). Moreover, the temporal patterns are considered as shift-invariant. This article is divided into two parts based on this model.… (More)