22 research outputs found
Analysis of the Spatial Distribution of Galaxies by Multiscale Methods
Galaxies are arranged in interconnected walls and filaments forming a cosmic
web encompassing huge, nearly empty, regions between the structures. Many
statistical methods have been proposed in the past in order to describe the
galaxy distribution and discriminate the different cosmological models. We
present in this paper results relative to the use of new statistical tools
using the 3D isotropic undecimated wavelet transform, the 3D ridgelet transform
and the 3D beamlet transform. We show that such multiscale methods produce a
new way to measure in a coherent and statistically reliable way the degree of
clustering, filamentarity, sheetedness, and voidedness of a datasetComment: 26 pages, 20 figures. Submitted to EURASIP Journal on Applied Signal
Processing (special issue on "Applications of Signal Processing in
Astrophysics and Cosmology"
Ionization Quenching Factor measurement of 1 keV to 25 keV protons in Isobutane gas mixture
Entropy and astronomical data analysis: Perspectives from multiresolution analysis
The Maximum Entropy Method is well-known and widely used
in image analysis in
astronomy. In its standard form it presents certain drawbacks,
such an underestimation
of the photometry. Various refinements of MEM have been proposed
over the years.
We review in this paper the main entropy functionals which have been proposed
and discuss each of them. We define, from a conceptual point of view,
what a good definition of entropy should be
in the framework of astronomical
data processing. We show how a definition of multiscale entropy fulfills
these requirements. We show how multiscale entropy can be used for
many applications, such as signal or image filtering,
multi-channel data filtering, deconvolution,
background fluctuation analysis, and astronomical image content analysis
Simultaneous cartoon and texture image inpainting using morphological component analysis (MCA)
AbstractThis paper describes a novel inpainting algorithm that is capable of filling in holes in overlapping texture and cartoon image layers. This algorithm is a direct extension of a recently developed sparse-representation-based image decomposition method called MCA (morphological component analysis), designed for the separation of linearly combined texture and cartoon layers in a given image (see [J.-L. Starck, M. Elad, D.L. Donoho, Image decomposition via the combination of sparse representations and a variational approach, IEEE Trans. Image Process. (2004), in press] and [J.-L. Starck, M. Elad, D.L. Donoho, Redundant multiscale transforms and their application for morphological component analysis, Adv. Imag. Electron Phys. (2004) 132]). In this extension, missing pixels fit naturally into the separation framework, producing separate layers as a by-product of the inpainting process. As opposed to the inpainting system proposed by Bertalmio et al., where image decomposition and filling-in stages were separated as two blocks in an overall system, the new approach considers separation, hole-filling, and denoising as one unified task. We demonstrate the performance of the new approach via several examples