7,250 research outputs found
Characterization and Application of Hard X-Ray Betatron Radiation Generated by Relativistic Electrons from a Laser-Wakefield Accelerator
The necessity for compact table-top x-ray sources with higher brightness,
shorter wavelength and shorter pulse duration has led to the development of
complementary sources based on laser-plasma accelerators, in contrast to
conventional accelerators. Relativistic interaction of short-pulse lasers with
underdense plasmas results in acceleration of electrons and in consequence in
the emission of spatially coherent radiation, which is known in the literature
as betatron radiation. In this article we report on our recent results in the
rapidly developing field of secondary x-ray radiation generated by high-energy
electron pulses. The betatron radiation is characterized with a novel setup
allowing to measure the energy, the spatial energy distribution in the
far-field of the beam and the source size in a single laser shot. Furthermore,
the polarization state is measured for each laser shot. In this way the emitted
betatron x-rays can be used as a non-invasive diagnostic tool to retrieve very
subtle information of the electron dynamics within the plasma wave. Parallel to
the experimental work, 3D particle-in-cell simulations were performed, proved
to be in good agreement with the experimental results.Comment: 38 pages, 19 figures, submitted to the Journal of Plasma Physic
Bottom-Up and Top-Down Reasoning with Hierarchical Rectified Gaussians
Convolutional neural nets (CNNs) have demonstrated remarkable performance in
recent history. Such approaches tend to work in a unidirectional bottom-up
feed-forward fashion. However, practical experience and biological evidence
tells us that feedback plays a crucial role, particularly for detailed spatial
understanding tasks. This work explores bidirectional architectures that also
reason with top-down feedback: neural units are influenced by both lower and
higher-level units.
We do so by treating units as rectified latent variables in a quadratic
energy function, which can be seen as a hierarchical Rectified Gaussian model
(RGs). We show that RGs can be optimized with a quadratic program (QP), that
can in turn be optimized with a recurrent neural network (with rectified linear
units). This allows RGs to be trained with GPU-optimized gradient descent. From
a theoretical perspective, RGs help establish a connection between CNNs and
hierarchical probabilistic models. From a practical perspective, RGs are well
suited for detailed spatial tasks that can benefit from top-down reasoning. We
illustrate them on the challenging task of keypoint localization under
occlusions, where local bottom-up evidence may be misleading. We demonstrate
state-of-the-art results on challenging benchmarks.Comment: To appear in CVPR 201
A Bayesian Hyperprior Approach for Joint Image Denoising and Interpolation, with an Application to HDR Imaging
Recently, impressive denoising results have been achieved by Bayesian
approaches which assume Gaussian models for the image patches. This improvement
in performance can be attributed to the use of per-patch models. Unfortunately
such an approach is particularly unstable for most inverse problems beyond
denoising. In this work, we propose the use of a hyperprior to model image
patches, in order to stabilize the estimation procedure. There are two main
advantages to the proposed restoration scheme: Firstly it is adapted to
diagonal degradation matrices, and in particular to missing data problems (e.g.
inpainting of missing pixels or zooming). Secondly it can deal with signal
dependent noise models, particularly suited to digital cameras. As such, the
scheme is especially adapted to computational photography. In order to
illustrate this point, we provide an application to high dynamic range imaging
from a single image taken with a modified sensor, which shows the effectiveness
of the proposed scheme.Comment: Some figures are reduced to comply with arxiv's size constraints.
Full size images are available as HAL technical report hal-01107519v5, IEEE
Transactions on Computational Imaging, 201
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