3,069 research outputs found
Proceedings of the second "international Traveling Workshop on Interactions between Sparse models and Technology" (iTWIST'14)
The implicit objective of the biennial "international - Traveling Workshop on
Interactions between Sparse models and Technology" (iTWIST) is to foster
collaboration between international scientific teams by disseminating ideas
through both specific oral/poster presentations and free discussions. For its
second edition, the iTWIST workshop took place in the medieval and picturesque
town of Namur in Belgium, from Wednesday August 27th till Friday August 29th,
2014. The workshop was conveniently located in "The Arsenal" building within
walking distance of both hotels and town center. iTWIST'14 has gathered about
70 international participants and has featured 9 invited talks, 10 oral
presentations, and 14 posters on the following themes, all related to the
theory, application and generalization of the "sparsity paradigm":
Sparsity-driven data sensing and processing; Union of low dimensional
subspaces; Beyond linear and convex inverse problem; Matrix/manifold/graph
sensing/processing; Blind inverse problems and dictionary learning; Sparsity
and computational neuroscience; Information theory, geometry and randomness;
Complexity/accuracy tradeoffs in numerical methods; Sparsity? What's next?;
Sparse machine learning and inference.Comment: 69 pages, 24 extended abstracts, iTWIST'14 website:
http://sites.google.com/site/itwist1
Process of image super-resolution
In this paper we explain a process of super-resolution reconstruction
allowing to increase the resolution of an image.The need for high-resolution
digital images exists in diverse domains, for example the medical and spatial
domains. The obtaining of high-resolution digital images can be made at the
time of the shooting, but it is often synonymic of important costs because of
the necessary material to avoid such costs, it is known how to use methods of
super-resolution reconstruction, consisting from one or several low resolution
images to obtain a high-resolution image. The american patent US 9208537
describes such an algorithm. A zone of one low-resolution image is isolated and
categorized according to the information contained in pixels forming the
borders of the zone. The category of it zone determines the type of
interpolation used to add pixels in aforementioned zone, to increase the
neatness of the images. It is also known how to reconstruct a low-resolution
image there high-resolution image by using a model of super-resolution
reconstruction whose learning is based on networks of neurons and on image or a
picture library. The demand of chinese patent CN 107563965 and the scientist
publication "Pixel Recursive Super Resolution", R. Dahl, M. Norouzi, J. Shlens
propose such methods. The aim of this paper is to demonstrate that it is
possible to reconstruct coherent human faces from very degraded pixelated
images with a very fast algorithm, more faster than compressed sensing (CS),
easier to compute and without deep learning, so without important technology
resources, i.e. a large database of thousands training images (see
arXiv:2003.13063).
This technological breakthrough has been patented in 2018 with the demand of
French patent FR 1855485 (https://patents.google.com/patent/FR3082980A1, see
the HAL reference https://hal.archives-ouvertes.fr/hal-01875898v1).Comment: 19 pages, 10 figure
Astronomical Data Analysis and Sparsity: from Wavelets to Compressed Sensing
Wavelets have been used extensively for several years now in astronomy for
many purposes, ranging from data filtering and deconvolution, to star and
galaxy detection or cosmic ray removal. More recent sparse representations such
ridgelets or curvelets have also been proposed for the detection of anisotropic
features such cosmic strings in the cosmic microwave background.
We review in this paper a range of methods based on sparsity that have been
proposed for astronomical data analysis. We also discuss what is the impact of
Compressed Sensing, the new sampling theory, in astronomy for collecting the
data, transferring them to the earth or reconstructing an image from incomplete
measurements.Comment: Submitted. Full paper will figures available at
http://jstarck.free.fr/IEEE09_SparseAstro.pd
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