463 research outputs found
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
Disparity and Optical Flow Partitioning Using Extended Potts Priors
This paper addresses the problems of disparity and optical flow partitioning
based on the brightness invariance assumption. We investigate new variational
approaches to these problems with Potts priors and possibly box constraints.
For the optical flow partitioning, our model includes vector-valued data and an
adapted Potts regularizer. Using the notation of asymptotically level stable
functions we prove the existence of global minimizers of our functionals. We
propose a modified alternating direction method of minimizers. This iterative
algorithm requires the computation of global minimizers of classical univariate
Potts problems which can be done efficiently by dynamic programming. We prove
that the algorithm converges both for the constrained and unconstrained
problems. Numerical examples demonstrate the very good performance of our
partitioning method
Multiscale Adaptive Representation of Signals: I. The Basic Framework
We introduce a framework for designing multi-scale, adaptive, shift-invariant
frames and bi-frames for representing signals. The new framework, called
AdaFrame, improves over dictionary learning-based techniques in terms of
computational efficiency at inference time. It improves classical multi-scale
basis such as wavelet frames in terms of coding efficiency. It provides an
attractive alternative to dictionary learning-based techniques for low level
signal processing tasks, such as compression and denoising, as well as high
level tasks, such as feature extraction for object recognition. Connections
with deep convolutional networks are also discussed. In particular, the
proposed framework reveals a drawback in the commonly used approach for
visualizing the activations of the intermediate layers in convolutional
networks, and suggests a natural alternative
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