In this paper, we address the problem of the separation of convolutive mixtures even in the case where the non-Gaussian source signals are not linear processes. In this context, we show that the contrast functions, previously introduced in the case of one-input/one-output blind deconvolution and then in linear source sepertion, allow one to separate the sources by a de#ation approach. Some part...

The relative Newton algorithm, previously proposed for quasi maximum likelihood blind source separation and blind deconvolution of one-dimensional signals is generalized for blind deconvolution of images. Smooth approximation of the absolute value is used in modelling the log probability density function, which is suitable for sparse sources. We propose a method of sparsification, which allows ...

Abstract We present a noise deconvolution technique to remove wide class of noises when performing arbitrary measurements on qubit systems. In particular, we derive the inverse map most common single noisy channels, and exploit it at data processing step obtain noise-free estimates observables evaluated system subject known noise. illustrate self-consistency check ensure that characterization i...

In spite of the huge literature on deconvolution problems, very little is done for hybrid contexts where signals are quantized. In this paper we undertake an information theoretic approach to the deconvolution problem of a simple integrator with quantized binary input and sampled noisy output. We recast it into a decoding problem and we propose and analyze (theoretically and numerically) some l...

In this paper we discuss the semi parametric statistical model for blind deconvolution. First we introduce a Lie Group to the manifold of noncausal FIR filters. Then blind deconvolution problem is formulated in the framework of a semiparametric model, and a family of estimating functions is derived for blind deconvolution. A natural gradient learning algorithm is developed for training noncausa...

The aim of the present Letter is to introduce a new blind deconvolution algorithm based on fixed-point optimization of a ‘Bussgang’-type cost function. The cost function relies on approximate Bayesian estimation achieved by an adaptive neuron. The main feature of the presented algorithm is fast convergence that guarantees good deconvolution performances with limited computational demand compare...

Sparse deconvolution is a classical subject in digital signal processing, having many practical applications. Support vector machine (SVM) algorithms show a series of characteristics, such as sparse solutions and implicit regularization, which make them attractive for solving sparse deconvolution problems. Here, a sparse deconvolution algorithm based on the SVM framework for signal processing i...

Pore space characterization of carbonate materials is fundamental importance to a wide range earth science and engineering applications. In this study, we show how confocal microscopy can be used as reliable tool visualize quantify the heterogeneous pore in materials. imagery, quality images controlled by various factors including choice fluorophore, objective lens, medium imaging. Our experime...