Multichannel blind deconvolution has received increasing attention during the last decade. Recently, Martone [3, 4] extended the super-exponential method proposed by Shalvi and Weinstein [1, 2] for single-channel blind deconvolution to multichannel blind deconvolution. However, the Martone extension suffers from two type of serious drawbacks. The objective of this paper is to obviate these draw...

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...

Blind deconvolution is an ubiquitous non-linear inverse problem in applications like wireless communications and image processing. This problem is generally ill-posed since signal identifiability is a key concern, and there have been efforts to use sparse models for regularizing blind deconvolution to promote signal identifiability. Part I of this two-part paper establishes a measure theoretica...

1 Summary The goal of blind deconvolution and source separation is to unravel the effects of an unknown linear transformation on a unknown signal source. For blind deconvolution, the transformation is a linear finite-impulse response (FIR) filter, and for blind source separation it is a matrix of mixing coefficients. A general architecture for these blind adaptive algorithms consists of an adju...

In this work, we propose a novel prior term for the regularization of blind deblurring methods. The proposed method introduces machine learning techniques into the blind deconvolution process. The proposed technique has sound mathematical foundations and is generic to many inverse problems. We demonstrate the usage of this regularizer within Bayesian blind deconvolution framework, and also inte...

The method described here performs blind deconvolution of the beamforming output in the frequency domain. To provide accurate blind deconvolution, sparsity priors are introduced with a smoothed `1/`2 regularization term. As the mean of the noise in the power spectrum domain depends on its variance in the time domain, the proposed method includes a variance estimation step, which allows more rob...

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...

Blind deconvolution problems arise in many image restoration applications. Most available blind deconvolution methods are iterative. Recently, Justen and Ramlau proposed a novel non-iterative blind deconvolution method. The method was derived under the assumption of periodic boundary conditions. These boundary conditions may introduce oscillatory artifacts into the computed restoration. We desc...