1,346 research outputs found
Roadmap on digital holography [Invited]
This Roadmap article on digital holography provides an overview of a vast array of research activities in the field of digital holography. The paper consists of a series of 25 sections from the prominent experts in digital holography presenting various aspects of the field on sensing, 3D imaging and displays, virtual and augmented reality, microscopy, cell identification, tomography, label-free live cell imaging, and other applications. Each section represents the vision of its author to describe the significant progress, potential impact, important developments, and challenging issues in the field of digital holography
Why Chromatic Imaging Matters
During the last two decades, the first generation of beam combiners at the
Very Large Telescope Interferometer has proved the importance of optical
interferometry for high-angular resolution astrophysical studies in the near-
and mid-infrared. With the advent of 4-beam combiners at the VLTI, the u-v
coverage per pointing increases significantly, providing an opportunity to use
reconstructed images as powerful scientific tools. Therefore, interferometric
imaging is already a key feature of the new generation of VLTI instruments, as
well as for other interferometric facilities like CHARA and JWST. It is thus
imperative to account for the current image reconstruction capabilities and
their expected evolutions in the coming years. Here, we present a general
overview of the current situation of optical interferometric image
reconstruction with a focus on new wavelength-dependent information,
highlighting its main advantages and limitations. As an Appendix we include
several cookbooks describing the usage and installation of several state-of-the
art image reconstruction packages. To illustrate the current capabilities of
the software available to the community, we recovered chromatic images, from
simulated MATISSE data, using the MCMC software SQUEEZE. With these images, we
aim at showing the importance of selecting good regularization functions and
their impact on the reconstruction.Comment: Accepted for publication in Experimental Astronomy as part of the
topical collection: Future of Optical-infrared Interferometry in Europ
A phase field method for tomographic reconstruction from limited data.
Classical tomographic reconstruction methods fail for problems in which there is
extreme temporal and spatial sparsity in the measured data. Reconstruction of coronal
mass ejections (CMEs), a space weather phenomenon with potential negative effects on
the Earth, is one such problem. However, the topological complexity of CMEs renders
recent limited data reconstruction methods inapplicable. We propose an energy function,
based on a phase field level set framework, for the joint segmentation and tomographic
reconstruction of CMEs from measurements acquired by coronagraphs, a type of solar
telescope. Our phase field model deals easily with complex topologies, and is more
robust than classical methods when the data are very sparse. We use a fast variational
algorithm that combines the finite element method with a trust region variant of Newton’s
method to minimize the energy. We compare the results obtained with our model to
classical regularized tomography for synthetic CME-like images
A phase field method for tomographic reconstruction from limited data
Classical tomographic reconstruction methods fail for problems in which there is extreme temporal and spatial sparsity in the measured data. Reconstruction of coronal mass ejections (CMEs), a space weather phenomenon with potential negative effects on the Earth, is one such problem. However, the topological complexity of CMEs renders recent limited data reconstruction methods inapplicable. We propose an energy function, based on a phase field level set framework, for the joint segmentation and tomographic reconstruction of CMEs from measurements acquired by coronagraphs, a type of solar telescope. Our phase field model deals easily with complex topologies, and is more robust than classical methods when the data are very sparse. We use a fast variational algorithm that combines the finite element method with a trust region variant of Newton’s method to minimize the energy. We compare the results obtained with our model to classical regularized tomography for synthetic CME-like images
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