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The 2012 West Nile virus (WNV) epidemic was the largest since 2003 and the North Texas region was the most heavily impacted. We conducted a serosurvey of blood donors from four counties in the Dallas–Fort Worth area to characterize the epidemic. Blood donor specimens collected in November 2012 were tested for WNV-specific antibodies. Donors positive for WNV-specific IgG, IgM, and neutralizing antibodies were considered to have been infected in 2012. This number was adjusted using a multi-step process that accounted for timing of IgM seroreversion determined from previous longitudinal studies of WNV-infected donors. Of 4971 donations screened, 139 (2·8%) were confirmed WNV IgG positive, and 69 (1·4%) had IgM indicating infection in 2012. After adjusting for timing of sampling and potential seroreversion, we estimated that 1·8% [95% confidence interval (CI) 1·5–2·2] of the adult population in the Dallas–Fort Worth area were infected during 2012. The resulting overall estimate for the ratio of infections to reported WNV neuroinvasive disease (WNND) cases was 238:1 (95% CI 192–290), with significantly increased risk of WNND in older age groups. These findings were very similar to previous estimates of infections per WNND case, indicating no change in virulence as WNV evolved into an endemic infection in the United States.
In this paper we give a survey of the methods we have developed for
multiple image deconvolution, with application to the reconstruction of
the images of the Large Binocular Telescope (LBT). We first describe
the main features of LBT and of the Fizeau interferometer, denoted
LINC-NIRVANA, that will be one of the basic instruments of the telescope.
It will allow to reach the resolution of a 22.8 m mirror by combining
different images taken with different orientations of the baseline. Next
we discuss the problem of multiple image deconvolution, that is crucial for
obtaining a unique high-resolution image from the multiple images provided
by LINC-NIRVANA. We present the state-of-the art of the methods based on
the Richardson-Lucy (RL) approach and we discuss topics such as
computational efficiency, correction of boundary effects and super-resolution.
Then, in the perspective of going beyond RL, we extend to the problem of
multiple image deconvolution the split gradient method (SGM) that is
a general approach to the design of iterative methods for the constrained
minimization of regularized functionals. Finally we present an application
of SGM to the regularized reconstruction of objects with high-dynamic range.
The different methods are illustrated with examples taken from the many
numerical experiments we performed on this problem.
IRAN is a method of beam-combination in the
hypertelescope imaging technique recently introduced by
Labeyrie in optical interferometry. We propose to observe the interferometric image in the pupil plane, performing multi-axial pupil plane interferometry. Imaging is performed in a combined pupil-plane where the point-source intensity distribution (PSID) tends towards a
pseudo Airy disc for a sufficiently large number of telescopes. The image is concentrated into the limited support of the output pupil of the individual telescopes, in which the object-image convolution relation is conserved. Specific deconvolution algorithms have been developped for IRAN hypertelescope imagery, based upon Lucy-like iterative techniques. We show that the classical (image plane) and IRAN (pupil plane) hypertelescope imaging techniques are equivalent if one uses optical fibers for beam transportation. An application to the VLT/VIDA concept is presented.
Founding on a physical transformation process described by a
Fredholm integral equation of the first kind, we first recall the
main difficulties appearing in linear inverse problems in the
continuous case as well as in the discrete case. We describe several
situations corresponding to various properties of the kernel of the
The need to take into account the properties of the solution not
contained in the model is then put in evidence. This leads to the
regularization principles for which the classical point of view as
well as the Bayesian interpretation are briefly reminded.
We then focus on the problem of deconvolution specially applied to
astronomical images. A complete model of image formation is
described in Section 4, and a general method allowing to derive
image restoration algorithms, the Split Gradient Method (SGM), is detailed
in Section 5.
We show in Section 6, that when this method is applied to the
likelihood maximization problems with positivity constraint, the
ISRA algorithm can be recovered in the case of the pure Gaussian
additive noise case, while in the case of pure Poisson noise, the
well known EM, Richardson-Lucy algorithm is easily obtained. The
method is then applied to the more realistic situation typical of
CCD detectors: Poisson photo-conversion noise plus Gaussian readout
noise, and to a new particular situation corresponding to data
acquired with Low Light Level CCD. Some numerical results are
exhibited in Section 7 for these two last cases. Finally, we show
how all these algorithms can be regularized in the context of the
SGM and we give a general conclusion.
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