110,978 research outputs found
LOFAR Sparse Image Reconstruction
Context. The LOw Frequency ARray (LOFAR) radio telescope is a giant digital
phased array interferometer with multiple antennas distributed in Europe. It
provides discrete sets of Fourier components of the sky brightness. Recovering
the original brightness distribution with aperture synthesis forms an inverse
problem that can be solved by various deconvolution and minimization methods
Aims. Recent papers have established a clear link between the discrete nature
of radio interferometry measurement and the "compressed sensing" (CS) theory,
which supports sparse reconstruction methods to form an image from the measured
visibilities. Empowered by proximal theory, CS offers a sound framework for
efficient global minimization and sparse data representation using fast
algorithms. Combined with instrumental direction-dependent effects (DDE) in the
scope of a real instrument, we developed and validated a new method based on
this framework Methods. We implemented a sparse reconstruction method in the
standard LOFAR imaging tool and compared the photometric and resolution
performance of this new imager with that of CLEAN-based methods (CLEAN and
MS-CLEAN) with simulated and real LOFAR data Results. We show that i) sparse
reconstruction performs as well as CLEAN in recovering the flux of point
sources; ii) performs much better on extended objects (the root mean square
error is reduced by a factor of up to 10); and iii) provides a solution with an
effective angular resolution 2-3 times better than the CLEAN images.
Conclusions. Sparse recovery gives a correct photometry on high dynamic and
wide-field images and improved realistic structures of extended sources (of
simulated and real LOFAR datasets). This sparse reconstruction method is
compatible with modern interferometric imagers that handle DDE corrections (A-
and W-projections) required for current and future instruments such as LOFAR
and SKAComment: Published in A&A, 19 pages, 9 figure
Multi-Pose Fusion for Sparse-View CT Reconstruction Using Consensus Equilibrium
CT imaging works by reconstructing an object of interest from a collection of
projections. Traditional methods such as filtered-back projection (FBP) work on
projection images acquired around a fixed rotation axis. However, for some CT
problems, it is desirable to perform a joint reconstruction from projection
data acquired from multiple rotation axes.
In this paper, we present Multi-Pose Fusion, a novel algorithm that performs
a joint tomographic reconstruction from CT scans acquired from multiple poses
of a single object, where each pose has a distinct rotation axis. Our approach
uses multi-agent consensus equilibrium (MACE), an extension of plug-and-play,
as a framework for integrating projection data from different poses. We apply
our method on simulated data and demonstrate that Multi-Pose Fusion can achieve
a better reconstruction result than single pose reconstruction.Comment: To appear in 58th Annual Allerton Conference on Communication,
Control, and Computin
Material Density Distribution of a Radial Symmetric Product from a Single X-Ray Radiograph
X-ray digital tomographic methods may be classified according to the number of projections and the angular coverage required to obtain the density distribution of the object under study. At one extremity stands computerized tomography which employs multiple projections and wide angular coverage (± π). At the other extremity stand reconstruction methods employing a single projection. As the number of projections decreases, the information provided for the reconstruction becomes more incomplete. The decrease in the information content may sometimes be compensated by the use of a priori knowledge about the product and thus alleviate to some extent the ill-posedness of the problem [l–4]
Multiple Projection Optical Diffusion Tomography with Plane Wave Illumination
We describe a new data collection scheme for optical diffusion tomography in
which plane wave illumination is combined with multiple projections in the slab
imaging geometry. Multiple projection measurements are performed by rotating
the slab around the sample. The advantage of the proposed method is that the
measured data can be much more easily fitted into the dynamic range of most
commonly used detectors. At the same time, multiple projections improve image
quality by mutually interchanging the depth and transverse directions, and the
scanned (detection) and integrated (illumination) surfaces. Inversion methods
are derived for image reconstructions with extremely large data sets. Numerical
simulations are performed for fixed and rotated slabs
EPiK-a Workflow for Electron Tomography in Kepler.
Scientific workflows integrate data and computing interfaces as configurable, semi-automatic graphs to solve a scientific problem. Kepler is such a software system for designing, executing, reusing, evolving, archiving and sharing scientific workflows. Electron tomography (ET) enables high-resolution views of complex cellular structures, such as cytoskeletons, organelles, viruses and chromosomes. Imaging investigations produce large datasets. For instance, in Electron Tomography, the size of a 16 fold image tilt series is about 65 Gigabytes with each projection image including 4096 by 4096 pixels. When we use serial sections or montage technique for large field ET, the dataset will be even larger. For higher resolution images with multiple tilt series, the data size may be in terabyte range. Demands of mass data processing and complex algorithms require the integration of diverse codes into flexible software structures. This paper describes a workflow for Electron Tomography Programs in Kepler (EPiK). This EPiK workflow embeds the tracking process of IMOD, and realizes the main algorithms including filtered backprojection (FBP) from TxBR and iterative reconstruction methods. We have tested the three dimensional (3D) reconstruction process using EPiK on ET data. EPiK can be a potential toolkit for biology researchers with the advantage of logical viewing, easy handling, convenient sharing and future extensibility
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