A robust algorithm for convolutive blind source separation in presence of noise

International audienceWe consider the blind source separation (BSS) problem in the noisy context. We propose a new methodology in order to enhance separation performances in terms of efficiency and robustness. Our approach consists in denoising the observed signals through the minimization of their total variation, and then minimizing divergence separation criteria combined with the total variation of the estimated source signals. We show by the way that the method leads to some projection problems that are solved by means of projected gradient algorithms. The efficiency and robustness of the proposed algorithm using Hellinger divergence are illustrated and compared with the classical mutual information approach through numerical simulations

A robust multi-frame super resolution based on curvature registration and second order variational regularization

International audienceMultiframe image super-resolution is a technique to obtain a high-resolution image by fusing a sequence of low-resolution ones. This paper deals with a new approach to robust super resolution based on regularization framework. Since registration is an important step that ensures the success of super resolution algorithms, must choose the most suitable method. We suggest a new algorithm specified at low resolution images with small deformations using fourth-order partial differential equations (PDE) regularization in the last step of super resolution. The deformations are not parametric and differs from one image to another. We use a curvature registration specially because image are slightly deformed. Experimental results show the robustness of the proposed method compared to classical super resolution methods

A NEW IMAGE DEBLURRING APPROACH USING A SPECIAL CONVOLUTION EXPANSION

International audienceThe deconvolution problem in image processing consists of reconstructing an original image from an observed and thus a degraded one. This degradation is often modelized as a linear operator plus an additive noise. The linear operator is called the blurring operator and the goal consists of deblurring the image. Very often, the blurring operator is modelized as a convolution whose kernel (the Point Spread Function) is not directly known in practice. In this paper, we first propose a new model for convolution and we validate it through computer simulations. Basically, we expend the kernel leading to a sequence of real coefficients in link with the moment problem. We particularly emphasize the radial isotropic case

On convolutive Blind Source Separation in a noisy context and a total variation regularization

International audienceWe propose a new strategy for improving classical Blind Source Separation (BSS) methods. This strategy consists in denois-ing both the observed signal and the estimated source signal , and is based on the minimization of regularized criterion which takes into account the Total Variation of the signal. We prove by the way that the method leads to a projection problem which is solved by means of projected gradient algorithm. The effectiveness and the robustness of the proposed separating process are shown on numerical examples

Blind noisy mixture separation for independent/dependent sources through a regularized criterion on copulas

International audienceThe paper introduces a new method for Blind Source Separation (BSS) in noisy instantaneous mixtures of both independent or dependent source component signals. This approach is based on the minimization of a regularized criterion. Precisely, it consists in combining the total variation method for denoising with the Kullback–Leibler divergence between copula densities. The latter takes advantage of the copula to model the structure of the dependence between signal components. The obtained algorithm achieves separation in a noisy context where standard BSS methods fail. The efficiency and robustness of the proposed approach are illustrated by numerical simulations