10,052 research outputs found
Video-rate computational super-resolution and integral imaging at longwave-infrared wavelengths
We report the first computational super-resolved, multi-camera integral
imaging at long-wave infrared (LWIR) wavelengths. A synchronized array of FLIR
Lepton cameras was assembled, and computational super-resolution and
integral-imaging reconstruction employed to generate video with light-field
imaging capabilities, such as 3D imaging and recognition of partially obscured
objects, while also providing a four-fold increase in effective pixel count.
This approach to high-resolution imaging enables a fundamental reduction in the
track length and volume of an imaging system, while also enabling use of
low-cost lens materials.Comment: Supplementary multimedia material in
http://dx.doi.org/10.6084/m9.figshare.530302
The VOLNA code for the numerical modelling of tsunami waves: generation, propagation and inundation
A novel tool for tsunami wave modelling is presented. This tool has the
potential of being used for operational purposes: indeed, the numerical code
\VOLNA is able to handle the complete life-cycle of a tsunami (generation,
propagation and run-up along the coast). The algorithm works on unstructured
triangular meshes and thus can be run in arbitrary complex domains. This paper
contains the detailed description of the finite volume scheme implemented in
the code. The numerical treatment of the wet/dry transition is explained. This
point is crucial for accurate run-up/run-down computations. Most existing
tsunami codes use semi-empirical techniques at this stage, which are not always
sufficient for tsunami hazard mitigation. Indeed the decision to evacuate
inhabitants is based on inundation maps which are produced with this type of
numerical tools. We present several realistic test cases that partially
validate our algorithm. Comparisons with analytical solutions and experimental
data are performed. Finally the main conclusions are outlined and the
perspectives for future research presented.Comment: 47 pages, 27 figures. Other author's papers can be downloaded at
http://www.lama.univ-savoie.fr/~dutykh
Forward model for quantitative pulse-echo speed-of-sound imaging
Computed ultrasound tomography in echo mode (CUTE) allows determining the
spatial distribution of speed-of-sound (SoS) inside tissue using handheld
pulse-echo ultrasound (US). This technique is based on measuring the changing
phase of beamformed echoes obtained under varying transmit (Tx) and/or receive
(Rx) steering angles. The SoS is reconstructed by inverting a forward model
describing how the spatial distribution of SoS is related to the spatial
distribution of the echo phase shift. CUTE holds promise as a novel diagnostic
modality that complements conventional US in a single, real-time handheld
system. Here we demonstrate that, in order to obtain robust quantitative
results, the forward model must contain two features that were not taken into
account so far: a) the phase shift must be detected between pairs of Tx and Rx
angles that are centred around a set of common mid-angles, and b) it must
account for an additional phase shift induced by the error of the reconstructed
position of echoes. In a phantom study mimicking liver imaging, this new model
leads to a substantially improved quantitative SoS reconstruction compared to
the model that has been used so far. The importance of the new model as a
prerequisite for an accurate diagnosis is corroborated in preliminary volunteer
results
Compressed sensing and finite rate of innovation for efficient data acquisition of quantitative acoustic microscopy images
La microscopie acoustique quantitative (MAQ) est une modalité d'imagerie bien établie qui donne accès à des cartes paramétriques 2D représentatives des propriétés mécaniques des tissus à une échelle microscopique. Dans la plupart des études sur MAQ, l'échantillons est scanné ligne par ligne (avec un pas de 2µm) à l'aide d'un transducteur à 250 MHz. Ce type d'acquisition permet d'obtenir un cube de données RF 3D, avec deux dimensions spatiales et une dimension temporelle. Chaque signal RF correspondant à une position spatiale dans l'échantillon permet d'estimer des paramètres acoustiques comme par exemple la vitesse du son ou l'impédance. Le temps d'acquisition en MAQ est directement proportionnel à la taille de l'échantillon et peut aller de quelques minutes à quelques dizaines de minutes. Afin d'assurer des conditions d'acquisition stables et étant donnée la sensibilité des échantillons à ces conditions, diminuer le temps d'acquisition est un des grand défis en MAQ. Afin de relever ce défi, ce travail de thèse propose plusieurs solutions basées sur l'échantillonnage compressé (EC) et la théories des signaux ayant un faible nombre de degré de liberté (finite rate of innovation - FRI, en anglais). Le principe de l'EC repose sur la parcimonie des données, sur l'échantillonnage incohérent de celles-ci et sur les algorithmes d'optimisation numérique. Dans cette thèse, les phénomènes physiques derrière la MAQ sont exploités afin de créer des modèles adaptés aux contraintes de l'EC et de la FRI. Plus particulièrement, ce travail propose plusieurs pistes d'application de l'EC en MAQ : un schéma d'acquisition spatiale innovant, un algorithme de reconstruction d'images exploitant les statistiques des coefficients en ondelettes des images paramétriques, un modèle FRI adapté aux signaux RF et un schéma d'acquisition compressée dans le domaine temporel.Quantitative acoustic microscopy (QAM) is a well-accepted modality for forming 2D parameter maps making use of mechanical properties of soft tissues at microscopic scales. In leading edge QAM studies, the sample is raster-scanned (spatial step size of 2µm) using a 250 MHz transducer resulting in a 3D RF data cube, and each RF signal for each spatial location is processed to obtain acoustic parameters, e.g., speed of sound or acoustic impedance. The scanning time directly depends on the sample size and can range from few minutes to tens of minutes. In order to maintain constant experimental conditions for the sensitive thin sectioned samples, the scanning time is an important practical issue. To deal with the current challenge, we propose the novel approach inspired by compressed sensing (CS) and finite rate of innovation (FRI). The success of CS relies on the sparsity of data under consideration, incoherent measurement and optimization technique. On the other hand, the idea behind FRI is supported by a signal model fully characterized as a limited number of parameters. From this perspective, taking into account the physics leading to data acquisition of QAM system, the QAM data can be regarded as an adequate application amenable to the state of the art technologies aforementioned. However, when it comes to the mechanical structure of QAM system which does not support canonical CS measurement manners on the one hand, and the compositions of the RF signal model unsuitable to existing FRI schemes on the other hand, the advanced frameworks are still not perfect methods to resolve the problems that we are facing. In this thesis, to overcome the limitations, a novel sensing framework for CS is presented in spatial domain: a recently proposed approximate message passing (AMP) algorithm is adapted to account for the underlying data statistics of samples sparsely collected by proposed scanning patterns. In time domain, as an approach for achieving an accurate recovery from a small set of samples of QAM RF signals, we employ sum of sincs (SoS) sampling kernel and autoregressive (AR) model estimator. The spiral scanning manner, introduced as an applicable sensing technique to QAM system, contributed to the significant reduction of the number of spatial samples when reconstructing speed of sound images of a human lymph node. Furthermore, the scanning time was also hugely saved due to the merit of the mechanical movement of the proposed sensing pattern. Together with the achievement in spatial domain, the introduction of SoS kernel and AR estimator responsible for an innovation rate sampling and a parameter estimation respectively led to dramatic reduction of the required number of samples per RF signal compared to a conventional approach. Finally, we showed that both data acquisition frameworks based on the CS and FRI can be combined into a single spatio-temporal solution to maximize the benefits stated above
Investigation of finite-volume methods to capture shocks and turbulence spectra in compressible flows
The aim of the present paper is to provide a comparison between several
finite-volume methods of different numerical accuracy: second-order Godunov
method with PPM interpolation and high-order finite-volume WENO method. The
results show that while on a smooth problem the high-order method perform
better than the second-order one, when the solution contains a shock all the
methods collapse to first-order accuracy. In the context of the decay of
compressible homogeneous isotropic turbulence with shocklets, the actual
overall order of accuracy of the methods reduces to second-order, despite the
use of fifth-order reconstruction schemes at cell interfaces. Most important,
results in terms of turbulent spectra are similar regardless of the numerical
methods employed, except that the PPM method fails to provide an accurate
representation in the high-frequency range of the spectra. It is found that
this specific issue comes from the slope-limiting procedure and a novel hybrid
PPM/WENO method is developed that has the ability to capture the turbulent
spectra with the accuracy of a high-order method, but at the cost of the
second-order Godunov method. Overall, it is shown that virtually the same
physical solution can be obtained much faster by refining a simulation with the
second-order method and carefully chosen numerical procedures, rather than
running a coarse high-order simulation. Our results demonstrate the importance
of evaluating the accuracy of a numerical method in terms of its actual
spectral dissipation and dispersion properties on mixed smooth/shock cases,
rather than by the theoretical formal order of convergence rate.Comment: This paper was previously composed of 2 parts, and this submission
was part 1. It is now replaced by the combined pape
Refraction-corrected ray-based inversion for three-dimensional ultrasound tomography of the breast
Ultrasound Tomography has seen a revival of interest in the past decade,
especially for breast imaging, due to improvements in both ultrasound and
computing hardware. In particular, three-dimensional ultrasound tomography, a
fully tomographic method in which the medium to be imaged is surrounded by
ultrasound transducers, has become feasible. In this paper, a comprehensive
derivation and study of a robust framework for large-scale bent-ray ultrasound
tomography in 3D for a hemispherical detector array is presented. Two
ray-tracing approaches are derived and compared. More significantly, the
problem of linking the rays between emitters and receivers, which is
challenging in 3D due to the high number of degrees of freedom for the
trajectory of rays, is analysed both as a minimisation and as a root-finding
problem. The ray-linking problem is parameterised for a convex detection
surface and three robust, accurate, and efficient ray-linking algorithms are
formulated and demonstrated. To stabilise these methods, novel
adaptive-smoothing approaches are proposed that control the conditioning of the
update matrices to ensure accurate linking. The nonlinear UST problem of
estimating the sound speed was recast as a series of linearised subproblems,
each solved using the above algorithms and within a steepest descent scheme.
The whole imaging algorithm was demonstrated to be robust and accurate on
realistic data simulated using a full-wave acoustic model and an anatomical
breast phantom, and incorporating the errors due to time-of-flight picking that
would be present with measured data. This method can used to provide a
low-artefact, quantitatively accurate, 3D sound speed maps. In addition to
being useful in their own right, such 3D sound speed maps can be used to
initialise full-wave inversion methods, or as an input to photoacoustic
tomography reconstructions
Digital forensic techniques for the reverse engineering of image acquisition chains
In recent years a number of new methods have been developed to detect image forgery. Most forensic techniques use footprints left on images to predict the history of the images. The images, however, sometimes could have gone through a series of processing and modification through their lifetime. It is therefore difficult to detect image tampering as the footprints could be distorted or removed over a complex chain of operations. In this research we propose digital forensic techniques that allow us to reverse engineer and determine history of images that have gone through chains of image acquisition and reproduction.
This thesis presents two different approaches to address the problem. In the first part we propose a novel theoretical framework for the reverse engineering of signal acquisition chains. Based on a simplified chain model, we describe how signals have gone in the chains at different stages using the theory of sampling signals with finite rate of innovation. Under particular conditions, our technique allows to detect whether a given signal has been reacquired through the chain. It also makes possible to predict corresponding important parameters of the chain using acquisition-reconstruction artefacts left on the signal.
The second part of the thesis presents our new algorithm for image recapture detection based on edge blurriness. Two overcomplete dictionaries are trained using the K-SVD approach to learn distinctive blurring patterns from sets of single captured and recaptured images. An SVM classifier is then built using dictionary approximation errors and the mean edge spread width from the training images. The algorithm, which requires no user intervention, was tested on a database that included more than 2500 high quality recaptured images. Our results show that our method achieves a performance rate that exceeds 99% for recaptured images and 94% for single captured images.Open Acces
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