17,294 research outputs found
Robust particle outline extraction and its application to digital on-line holography
Peer reviewedPostprin
Visual Quality Enhancement in Optoacoustic Tomography using Active Contour Segmentation Priors
Segmentation of biomedical images is essential for studying and
characterizing anatomical structures, detection and evaluation of pathological
tissues. Segmentation has been further shown to enhance the reconstruction
performance in many tomographic imaging modalities by accounting for
heterogeneities of the excitation field and tissue properties in the imaged
region. This is particularly relevant in optoacoustic tomography, where
discontinuities in the optical and acoustic tissue properties, if not properly
accounted for, may result in deterioration of the imaging performance.
Efficient segmentation of optoacoustic images is often hampered by the
relatively low intrinsic contrast of large anatomical structures, which is
further impaired by the limited angular coverage of some commonly employed
tomographic imaging configurations. Herein, we analyze the performance of
active contour models for boundary segmentation in cross-sectional optoacoustic
tomography. The segmented mask is employed to construct a two compartment model
for the acoustic and optical parameters of the imaged tissues, which is
subsequently used to improve accuracy of the image reconstruction routines. The
performance of the suggested segmentation and modeling approach are showcased
in tissue-mimicking phantoms and small animal imaging experiments.Comment: Accepted for publication in IEEE Transactions on Medical Imagin
Adaptive Measurement Network for CS Image Reconstruction
Conventional compressive sensing (CS) reconstruction is very slow for its
characteristic of solving an optimization problem. Convolu- tional neural
network can realize fast processing while achieving compa- rable results. While
CS image recovery with high quality not only de- pends on good reconstruction
algorithms, but also good measurements. In this paper, we propose an adaptive
measurement network in which measurement is obtained by learning. The new
network consists of a fully-connected layer and ReconNet. The fully-connected
layer which has low-dimension output acts as measurement. We train the
fully-connected layer and ReconNet simultaneously and obtain adaptive
measurement. Because the adaptive measurement fits dataset better, in contrast
with random Gaussian measurement matrix, under the same measuremen- t rate, it
can extract the information of scene more efficiently and get better
reconstruction results. Experiments show that the new network outperforms the
original one.Comment: 11pages,8figure
Graph Spectral Image Processing
Recent advent of graph signal processing (GSP) has spurred intensive studies
of signals that live naturally on irregular data kernels described by graphs
(e.g., social networks, wireless sensor networks). Though a digital image
contains pixels that reside on a regularly sampled 2D grid, if one can design
an appropriate underlying graph connecting pixels with weights that reflect the
image structure, then one can interpret the image (or image patch) as a signal
on a graph, and apply GSP tools for processing and analysis of the signal in
graph spectral domain. In this article, we overview recent graph spectral
techniques in GSP specifically for image / video processing. The topics covered
include image compression, image restoration, image filtering and image
segmentation
Coding of details in very low bit-rate video systems
In this paper, the importance of including small image features at the initial levels of a progressive second generation video coding scheme is presented. It is shown that a number of meaningful small features called details should be coded, even at very low data bit-rates, in order to match their perceptual significance to the human visual system. We propose a method for extracting, perceptually selecting and coding of visual details in a video sequence using morphological techniques. Its application in the framework of a multiresolution segmentation-based coding algorithm yields better results than pure segmentation techniques at higher compression ratios, if the selection step fits some main subjective requirements. Details are extracted and coded separately from the region structure and included in the reconstructed images in a later stage. The bet of considering the local background of a given detail for its perceptual selection breaks the concept ofPeer ReviewedPostprint (published version
Joint Image Reconstruction and Segmentation Using the Potts Model
We propose a new algorithmic approach to the non-smooth and non-convex Potts
problem (also called piecewise-constant Mumford-Shah problem) for inverse
imaging problems. We derive a suitable splitting into specific subproblems that
can all be solved efficiently. Our method does not require a priori knowledge
on the gray levels nor on the number of segments of the reconstruction.
Further, it avoids anisotropic artifacts such as geometric staircasing. We
demonstrate the suitability of our method for joint image reconstruction and
segmentation. We focus on Radon data, where we in particular consider limited
data situations. For instance, our method is able to recover all segments of
the Shepp-Logan phantom from angular views only. We illustrate the
practical applicability on a real PET dataset. As further applications, we
consider spherical Radon data as well as blurred data
Simulating binary neutron stars: dynamics and gravitational waves
We model two mergers of orbiting binary neutron stars, the first forming a
black hole and the second a differentially rotating neutron star. We extract
gravitational waveforms in the wave zone. Comparisons to a post-Newtonian
analysis allow us to compute the orbital kinematics, including trajectories and
orbital eccentricities. We verify our code by evolving single stars and
extracting radial perturbative modes, which compare very well to results from
perturbation theory. The Einstein equations are solved in a first order
reduction of the generalized harmonic formulation, and the fluid equations are
solved using a modified convex essentially non-oscillatory method. All
calculations are done in three spatial dimensions without symmetry assumptions.
We use the \had computational infrastructure for distributed adaptive mesh
refinement.Comment: 14 pages, 16 figures. Added one figure from previous version;
corrected typo
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
Towards visualisation of central-cell-effects in scanning-tunnelling-microscope images of subsurface dopant qubits in silicon
Atomic-scale understanding of phosphorous donor wave functions underpins the
design and optimisation of silicon based quantum devices. The accuracy of
large-scale theoretical methods to compute donor wave functions is dependent on
descriptions of central-cell-corrections, which are empirically fitted to match
experimental binding energies, or other quantities associated with the global
properties of the wave function. Direct approaches to understanding such
effects in donor wave functions are of great interest. Here, we apply a
comprehensive atomistic theoretical framework to compute scanning tunnelling
microscopy (STM) images of subsurface donor wave functions with two
central-cell-correction formalisms previously employed in the literature. The
comparison between central-cell models based on real-space image features and
the Fourier transform profiles indicate that the central-cell effects are
visible in the simulated STM images up to ten monolayers below the silicon
surface. Our study motivates a future experimental investigation of the
central-cell effects via STM imaging technique with potential of fine tuning
theoretical models, which could play a vital role in the design of donor-based
quantum systems in scalable quantum computer architectures.Comment: Nanoscale 201
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