326 research outputs found
Quasi-Nonparametric Blind Inversion of Wiener Systems
An e cient procedure for the blind inversion of a nonlinear Wiener system is proposed. We proved that the
problem can be expressed as a problem of blind source separation in nonlinear mixtures, for which a solution
has been recently proposed. Based on a quasi-nonparametric relative gradient descent, the proposed algorithm
can perform e ciently even in the presence of hard distortions
Blind channel deconvolution of real world signals using source separation techniques
In this paper we present a method for blind deconvolution of linear
channels based on source separation techniques, for real word signals. This
technique applied to blind deconvolution problems is based in exploiting not
the spatial independence between signals but the temporal independence between
samples of the signal. Our objective is to minimize the mutual information
between samples of the output in order to retrieve the original signal. In
order to make use of use this idea the input signal must be a non-Gaussian i.i.d.
signal. Because most real world signals do not have this i.i.d. nature, we will
need to preprocess the original signal before the transmission into the channel.
Likewise we should assure that the transmitted signal has non-Gaussian statistics
in order to achieve the correct function of the algorithm. The strategy used
for this preprocessing will be presented in this paper. If the receiver has the inverse
of the preprocess, the original signal can be reconstructed without the
convolutive distortion
Initialisation of Nonlinearities for PNL and Wiener systems Inversion
This paper proposes a very fast method for blindly initial-
izing a nonlinear mapping which transforms a sum of random variables.
The method provides a surprisingly good approximation even when the
basic assumption is not fully satis¯ed. The method can been used success-
fully for initializing nonlinearity in post-nonlinear mixtures or in Wiener
system inversion, for improving algorithm speed and convergence
A Canonical Genetic Algorithm for Blind Inversion of Linear Channels
It is well known the relationship between source separation and blind
deconvolution: If a filtered version of an unknown i.i.d. signal is observed, temporal
independence between samples can be used to retrieve the original signal,
in the same manner as spatial independence is used for source separation. In
this paper we propose the use of a Genetic Algorithm (GA) to blindly invert
linear channels. The use of GA is justified in the case of small number of samples,
where other gradient-like methods fails because of poor estimation of statistics
Improving algorithm speed in PNL mixture separation and Wiener system inversion
This paper proposes a very simple method for increasing
the algorithm speed for separating sources from PNL mixtures
or invertingWiener systems. The method is based on a
pertinent initialization of the inverse system, whose computational
cost is very low. The nonlinear part is roughly approximated
by pushing the observations to be Gaussian; this
method provides a surprisingly good approximation even
when the basic assumption is not fully satisfied. The linear
part is initialized so that outputs are decorrelated. Experiments
shows the impressive speed improvement
Fast Approximation of Nonlinearities for improving inversion algorithms of PNL mixtures and Wiener systems
This paper proposes a very fast method for blindly approximating a nonlinear mapping
which transforms a sum of random variables. The estimation is surprisingly
good even when the basic assumption is not satisfied.We use the method for providing
a good initialization for inverting post-nonlinear mixtures and Wiener systems.
Experiments show that the algorithm speed is strongly improved and the asymptotic
performance is preserved with a very low extra computational cost
Parametric Approach to Blind Deconvolution of Nonlinear Channels
A parametric procedure for the blind inversion of nonlinear channels is
proposed, based on a recent method of blind source separation in nonlinear
mixtures. Experiments show that the proposed algorithms perform
efficiently, even in the presence of hard distortion. The method, based on
the minimization of the output mutual information, needs the knowledge of
log-derivative of input distribution (the so-called score function). Each
algorithm consists of three adaptive blocks: one devoted to adaptive
estimation of the score function, and two other blocks estimating the
inverses of the linear and nonlinear parts of the channel, (quasi-)optimally
adapted using the estimated score functions. This paper is mainly
concerned by the nonlinear part, for which we propose two parametric
models, the first based on a polynomial model and the second on a neural
network, while [14, 15] proposed non-parametric approaches
Blind deconvolution of medical ultrasound images: parametric inverse filtering approach
©2007 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or distribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE. This material is presented to ensure timely dissemination of scholarly and technical work. Copyright and all rights therein are retained by authors or by other copyright holders. All persons copying this information are expected to adhere to the terms and constraints invoked by each author's copyright. In most cases, these works may not be reposted without the explicit permission of the copyright holder.DOI: 10.1109/TIP.2007.910179The problem of reconstruction of ultrasound images by means of blind deconvolution has long been recognized as one of the central problems in medical ultrasound imaging. In this paper, this problem is addressed via proposing a blind deconvolution method which is innovative in several ways. In particular, the method is based on parametric inverse filtering, whose parameters are optimized using two-stage processing. At the first stage, some partial information on the point spread function is recovered. Subsequently, this information is used to explicitly constrain the spectral shape of the inverse filter. From this perspective, the proposed methodology can be viewed as a ldquohybridizationrdquo of two standard strategies in blind deconvolution, which are based on either concurrent or successive estimation of the point spread function and the image of interest. Moreover, evidence is provided that the ldquohybridrdquo approach can outperform the standard ones in a number of important practical cases. Additionally, the present study introduces a different approach to parameterizing the inverse filter. Specifically, we propose to model the inverse transfer function as a member of a principal shift-invariant subspace. It is shown that such a parameterization results in considerably more stable reconstructions as compared to standard parameterization methods. Finally, it is shown how the inverse filters designed in this way can be used to deconvolve the images in a nonblind manner so as to further improve their quality. The usefulness and practicability of all the introduced innovations are proven in a series of both in silico and in vivo experiments. Finally, it is shown that the proposed deconvolution algorithms are capable of improving the resolution of ultrasound images by factors of 2.24 or 6.52 (as judged by the autocorrelation criterion) depending on the type of regularization method used
Source separation techniques applied to linear prediction
The prediction filters are well known models for signal
estimation, in communications, control and many others
areas. The classical method for deriving linear
prediction coding (LPC) filters is often based on the
minimization of a mean square error (MSE).
Consequently, second order statistics are only required,
but the estimation is only optimal if the residue is
independent and identically distributed (iid) Gaussian.
In this paper, we derive the ML estimate of the
prediction filter. Relationships with robust estimation of
auto-regressive (AR) processes, with blind
deconvolution and with source separation based on
mutual information minimization are then detailed. The
algorithm, based on the minimization of a high-order
statistics criterion, uses on-line estimation of the residue
statistics. Experimental results emphasize on the
interest of this approach
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