1,555 research outputs found
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
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
Recent Progress in Image Deblurring
This paper comprehensively reviews the recent development of image
deblurring, including non-blind/blind, spatially invariant/variant deblurring
techniques. Indeed, these techniques share the same objective of inferring a
latent sharp image from one or several corresponding blurry images, while the
blind deblurring techniques are also required to derive an accurate blur
kernel. Considering the critical role of image restoration in modern imaging
systems to provide high-quality images under complex environments such as
motion, undesirable lighting conditions, and imperfect system components, image
deblurring has attracted growing attention in recent years. From the viewpoint
of how to handle the ill-posedness which is a crucial issue in deblurring
tasks, existing methods can be grouped into five categories: Bayesian inference
framework, variational methods, sparse representation-based methods,
homography-based modeling, and region-based methods. In spite of achieving a
certain level of development, image deblurring, especially the blind case, is
limited in its success by complex application conditions which make the blur
kernel hard to obtain and be spatially variant. We provide a holistic
understanding and deep insight into image deblurring in this review. An
analysis of the empirical evidence for representative methods, practical
issues, as well as a discussion of promising future directions are also
presented.Comment: 53 pages, 17 figure
Speaker recognition improvement using blind inversion of distortions
In this paper we propose the inversion of nonlinear
distortions in order to improve the recognition rates of a
speaker recognizer system. We study the effect of
saturations on the test signals, trying to take into account
real situations where the training material has been recorded
in a controlled situation but the testing signals present some
mismatch with the input signal level (saturations). The
experimental results shows that a combination of several
strategies can improve the recognition rates with saturated
test sentences from 80% to 89.39%, while the results with
clean speech (without saturation) is 87.76% for one
microphone
A blind deconvolution approach to recover effective connectivity brain networks from resting state fMRI data
A great improvement to the insight on brain function that we can get from
fMRI data can come from effective connectivity analysis, in which the flow of
information between even remote brain regions is inferred by the parameters of
a predictive dynamical model. As opposed to biologically inspired models, some
techniques as Granger causality (GC) are purely data-driven and rely on
statistical prediction and temporal precedence. While powerful and widely
applicable, this approach could suffer from two main limitations when applied
to BOLD fMRI data: confounding effect of hemodynamic response function (HRF)
and conditioning to a large number of variables in presence of short time
series. For task-related fMRI, neural population dynamics can be captured by
modeling signal dynamics with explicit exogenous inputs; for resting-state fMRI
on the other hand, the absence of explicit inputs makes this task more
difficult, unless relying on some specific prior physiological hypothesis. In
order to overcome these issues and to allow a more general approach, here we
present a simple and novel blind-deconvolution technique for BOLD-fMRI signal.
Coming to the second limitation, a fully multivariate conditioning with short
and noisy data leads to computational problems due to overfitting. Furthermore,
conceptual issues arise in presence of redundancy. We thus apply partial
conditioning to a limited subset of variables in the framework of information
theory, as recently proposed. Mixing these two improvements we compare the
differences between BOLD and deconvolved BOLD level effective networks and draw
some conclusions
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