1,876 research outputs found
Convolutional Deblurring for Natural Imaging
In this paper, we propose a novel design of image deblurring in the form of
one-shot convolution filtering that can directly convolve with naturally
blurred images for restoration. The problem of optical blurring is a common
disadvantage to many imaging applications that suffer from optical
imperfections. Despite numerous deconvolution methods that blindly estimate
blurring in either inclusive or exclusive forms, they are practically
challenging due to high computational cost and low image reconstruction
quality. Both conditions of high accuracy and high speed are prerequisites for
high-throughput imaging platforms in digital archiving. In such platforms,
deblurring is required after image acquisition before being stored, previewed,
or processed for high-level interpretation. Therefore, on-the-fly correction of
such images is important to avoid possible time delays, mitigate computational
expenses, and increase image perception quality. We bridge this gap by
synthesizing a deconvolution kernel as a linear combination of Finite Impulse
Response (FIR) even-derivative filters that can be directly convolved with
blurry input images to boost the frequency fall-off of the Point Spread
Function (PSF) associated with the optical blur. We employ a Gaussian low-pass
filter to decouple the image denoising problem for image edge deblurring.
Furthermore, we propose a blind approach to estimate the PSF statistics for two
Gaussian and Laplacian models that are common in many imaging pipelines.
Thorough experiments are designed to test and validate the efficiency of the
proposed method using 2054 naturally blurred images across six imaging
applications and seven state-of-the-art deconvolution methods.Comment: 15 pages, for publication in IEEE Transaction Image Processin
The Harmonic Analysis of Kernel Functions
Kernel-based methods have been recently introduced for linear system
identification as an alternative to parametric prediction error methods.
Adopting the Bayesian perspective, the impulse response is modeled as a
non-stationary Gaussian process with zero mean and with a certain kernel (i.e.
covariance) function. Choosing the kernel is one of the most challenging and
important issues. In the present paper we introduce the harmonic analysis of
this non-stationary process, and argue that this is an important tool which
helps in designing such kernel. Furthermore, this analysis suggests also an
effective way to approximate the kernel, which allows to reduce the
computational burden of the identification procedure
A Primal-Dual Proximal Algorithm for Sparse Template-Based Adaptive Filtering: Application to Seismic Multiple Removal
Unveiling meaningful geophysical information from seismic data requires to
deal with both random and structured "noises". As their amplitude may be
greater than signals of interest (primaries), additional prior information is
especially important in performing efficient signal separation. We address here
the problem of multiple reflections, caused by wave-field bouncing between
layers. Since only approximate models of these phenomena are available, we
propose a flexible framework for time-varying adaptive filtering of seismic
signals, using sparse representations, based on inaccurate templates. We recast
the joint estimation of adaptive filters and primaries in a new convex
variational formulation. This approach allows us to incorporate plausible
knowledge about noise statistics, data sparsity and slow filter variation in
parsimony-promoting wavelet frames. The designed primal-dual algorithm solves a
constrained minimization problem that alleviates standard regularization issues
in finding hyperparameters. The approach demonstrates significantly good
performance in low signal-to-noise ratio conditions, both for simulated and
real field seismic data
Exact regularized point particle method for multi-phase flows in the two-way coupling regime
Particulate flows have been largely studied under the simplifying assumptions
of one-way coupling regime where the disperse phase do not react-back on the
carrier fluid. In the context of turbulent flows, many non trivial phenomena
such as small scales particles clustering or preferential spatial accumulation
have been explained and understood. A more complete view of multiphase flows
can be gained calling into play two-way coupling effects, i.e. by accounting
for the inter-phase momentum exchange between the carrier and the suspended
phase, certainly relevant at increasing mass loading. In such regime, partially
investigated in the past by the so-called Particle In Cell (PIC) method, much
is still to be learned about the dynamics of the disperse phase and the ensuing
alteration of the carrier flow.
In this paper we present a new methodology rigorously designed to capture the
inter-phase momentum exchange for particles smaller than the smallest
hydrodynamical scale, e.g. the Kolmogorov scale in a turbulent flow. In fact,
the momentum coupling mechanism exploits the unsteady Stokes flow around a
small rigid sphere where the transient disturbance produced by each particle is
evaluated in a closed form. The particles are described as lumped, point masses
which would lead to the appearance of singularities. A rigorous regularization
procedure is conceived to extract the physically relevant interactions between
particles and fluid which avoids any "ah hoc" assumption. The approach is
suited for high efficiency implementation on massively parallel machines since
the transient disturbance produced by the particles is strongly localized in
space around the actual particle position. As will be shown, hundred thousands
particles can therefore be handled at an affordable computational cost as
demonstrated by a preliminary application to a particle laden turbulent shear
flow.Comment: Submitted to Journal of Fluid Mechanics, 56 pages, 15 figure
A constrained-based optimization approach for seismic data recovery problems
Random and structured noise both affect seismic data, hiding the reflections
of interest (primaries) that carry meaningful geophysical interpretation. When
the structured noise is composed of multiple reflections, its adaptive
cancellation is obtained through time-varying filtering, compensating
inaccuracies in given approximate templates. The under-determined problem can
then be formulated as a convex optimization one, providing estimates of both
filters and primaries. Within this framework, the criterion to be minimized
mainly consists of two parts: a data fidelity term and hard constraints
modeling a priori information. This formulation may avoid, or at least
facilitate, some parameter determination tasks, usually difficult to perform in
inverse problems. Not only classical constraints, such as sparsity, are
considered here, but also constraints expressed through hyperplanes, onto which
the projection is easy to compute. The latter constraints lead to improved
performance by further constraining the space of geophysically sound solutions.Comment: International Conference on Acoustics, Speech and Signal Processing
(ICASSP 2014); Special session "Seismic Signal Processing
Efficient Multidimensional Regularization for Volterra Series Estimation
This paper presents an efficient nonparametric time domain nonlinear system
identification method. It is shown how truncated Volterra series models can be
efficiently estimated without the need of long, transient-free measurements.
The method is a novel extension of the regularization methods that have been
developed for impulse response estimates of linear time invariant systems. To
avoid the excessive memory needs in case of long measurements or large number
of estimated parameters, a practical gradient-based estimation method is also
provided, leading to the same numerical results as the proposed Volterra
estimation method. Moreover, the transient effects in the simulated output are
removed by a special regularization method based on the novel ideas of
transient removal for Linear Time-Varying (LTV) systems. Combining the proposed
methodologies, the nonparametric Volterra models of the cascaded water tanks
benchmark are presented in this paper. The results for different scenarios
varying from a simple Finite Impulse Response (FIR) model to a 3rd degree
Volterra series with and without transient removal are compared and studied. It
is clear that the obtained models capture the system dynamics when tested on a
validation dataset, and their performance is comparable with the white-box
(physical) models
Selective attention and speech processing in the cortex
In noisy and complex environments, human listeners must segregate the mixture of sound sources arriving at their ears and selectively attend a single source, thereby solving a computationally difficult problem called the cocktail party problem. However, the neural mechanisms underlying these computations are still largely a mystery. Oscillatory synchronization of neuronal activity between cortical areas is thought to provide a crucial role in facilitating information transmission between spatially separated populations of neurons, enabling the formation of functional networks.
In this thesis, we seek to analyze and model the functional neuronal networks underlying attention to speech stimuli and find that the Frontal Eye Fields play a central 'hub' role in the auditory spatial attention network in a cocktail party experiment. We use magnetoencephalography (MEG) to measure neural signals with high temporal precision, while sampling from the whole cortex. However, several methodological issues arise when undertaking functional connectivity analysis with MEG data. Specifically, volume conduction of electrical and magnetic fields in the brain complicates interpretation of results. We compare several approaches through simulations, and analyze the trade-offs among various measures of neural phase-locking in the presence of volume conduction. We use these insights to study functional networks in a cocktail party experiment.
We then construct a linear dynamical system model of neural responses to ongoing speech. Using this model, we are able to correctly predict which of two speakers is being attended by a listener. We then apply this model to data from a task where people were attending to stories with synchronous and scrambled videos of the speakers' faces to explore how the presence of visual information modifies the underlying neuronal mechanisms of speech perception. This model allows us to probe neural processes as subjects listen to long stimuli, without the need for a trial-based experimental design. We model the neural activity with latent states, and model the neural noise spectrum and functional connectivity with multivariate autoregressive dynamics, along with impulse responses for external stimulus processing. We also develop a new regularized Expectation-Maximization (EM) algorithm to fit this model to electroencephalography (EEG) data
Hydro-micromechanical modeling of wave propagation in saturated granular media
Biot's theory predicts the wave velocities of a saturated poroelastic
granular medium from the elastic properties, density and geometry of its dry
solid matrix and the pore fluid, neglecting the interaction between constituent
particles and local flow. However, when the frequencies become high and the
wavelengths comparable with particle size, the details of the microstructure
start to play an important role. Here, a novel hydro-micromechanical numerical
model is proposed by coupling the lattice Boltzmann method (LBM) with the
discrete element method (DEM. The model allows to investigate the details of
the particle-fluid interaction during propagation of elastic waves While the
DEM is tracking the translational and rotational motion of each solid particle,
the LBM can resolve the pore-scale hydrodynamics. Solid and fluid phases are
two-way coupled through momentum exchange. The coupling scheme is benchmarked
with the terminal velocity of a single sphere settling in a fluid. To mimic a
pressure wave entering a saturated granular medium, an oscillating pressure
boundary condition on the fluid is implemented and benchmarked with
one-dimensional wave equations. Using a face centered cubic structure, the
effects of input waveforms and frequencies on the dispersion relations are
investigated. Finally, the wave velocities at various effective confining
pressures predicted by the numerical model are compared with with Biot's
analytical solution, and a very good agreement is found. In addition to the
pressure and shear waves, slow compressional waves are observed in the
simulations, as predicted by Biot's theory.Comment: Manuscript submitted to International Journal for Numerical and
Analytical Methods in Geomechanic
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