155,335 research outputs found

    Sparse component separation for accurate CMB map estimation

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    The Cosmological Microwave Background (CMB) is of premier importance for the cosmologists to study the birth of our universe. Unfortunately, most CMB experiments such as COBE, WMAP or Planck do not provide a direct measure of the cosmological signal; CMB is mixed up with galactic foregrounds and point sources. For the sake of scientific exploitation, measuring the CMB requires extracting several different astrophysical components (CMB, Sunyaev-Zel'dovich clusters, galactic dust) form multi-wavelength observations. Mathematically speaking, the problem of disentangling the CMB map from the galactic foregrounds amounts to a component or source separation problem. In the field of CMB studies, a very large range of source separation methods have been applied which all differ from each other in the way they model the data and the criteria they rely on to separate components. Two main difficulties are i) the instrument's beam varies across frequencies and ii) the emission laws of most astrophysical components vary across pixels. This paper aims at introducing a very accurate modeling of CMB data, based on sparsity, accounting for beams variability across frequencies as well as spatial variations of the components' spectral characteristics. Based on this new sparse modeling of the data, a sparsity-based component separation method coined Local-Generalized Morphological Component Analysis (L-GMCA) is described. Extensive numerical experiments have been carried out with simulated Planck data. These experiments show the high efficiency of the proposed component separation methods to estimate a clean CMB map with a very low foreground contamination, which makes L-GMCA of prime interest for CMB studies.Comment: submitted to A&

    Means of confusion: how pixel noise affects shear estimates for weak gravitational lensing

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    Weak-lensing shear estimates show a troublesome dependence on the apparent brightness of the galaxies used to measure the ellipticity: In several studies, the amplitude of the inferred shear falls sharply with decreasing source significance. This dependence limits the overall ability of upcoming large weak-lensing surveys to constrain cosmological parameters. We seek to provide a concise overview of the impact of pixel noise on weak-lensing measurements, covering the entire path from noisy images to shear estimates. We show that there are at least three distinct layers, where pixel noise not only obscures but biases the outcome of the measurements: 1) the propagation of pixel noise to the non-linear observable ellipticity; 2) the response of the shape-measurement methods to limited amount of information extractable from noisy images; and 3) the reaction of shear estimation statistics to the presence of noise and outliers in the measured ellipticities. We identify and discuss several fundamental problems and show that each of them is able to introduce biases in the range of a few tenths to a few percent for galaxies with typical significance levels. Furthermore, all of these biases do not only depend on the brightness of galaxies but also on their ellipticity, with more elliptical galaxies often being harder to measure correctly. We also discuss existing possibilities to mitigate and novel ideas to avoid the biases induced by pixel noise. We present a new shear estimator that shows a more robust performance for noisy ellipticity samples. Finally, we release the open-source python code to predict and efficiently sample from the noisy ellipticity distribution and the shear estimators used in this work at https://github.com/pmelchior/epsnoiseComment: integrated the origin of the moment correlation (thanks to Alan Heavens). source code at https://github.com/pmelchior/epsnois

    FASTLens (FAst STatistics for weak Lensing) : Fast method for Weak Lensing Statistics and map making

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    With increasingly large data sets, weak lensing measurements are able to measure cosmological parameters with ever greater precision. However this increased accuracy also places greater demands on the statistical tools used to extract the available information. To date, the majority of lensing analyses use the two point-statistics of the cosmic shear field. These can either be studied directly using the two-point correlation function, or in Fourier space, using the power spectrum. But analyzing weak lensing data inevitably involves the masking out of regions or example to remove bright stars from the field. Masking out the stars is common practice but the gaps in the data need proper handling. In this paper, we show how an inpainting technique allows us to properly fill in these gaps with only NlogNN \log N operations, leading to a new image from which we can compute straight forwardly and with a very good accuracy both the pow er spectrum and the bispectrum. We propose then a new method to compute the bispectrum with a polar FFT algorithm, which has the main advantage of avoiding any interpolation in the Fourier domain. Finally we propose a new method for dark matter mass map reconstruction from shear observations which integrates this new inpainting concept. A range of examples based on 3D N-body simulations illustrates the results.Comment: Final version accepted by MNRAS. The FASTLens software is available from the following link : http://irfu.cea.fr/Ast/fastlens.software.ph

    Distributed video coding for wireless video sensor networks: a review of the state-of-the-art architectures

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    Distributed video coding (DVC) is a relatively new video coding architecture originated from two fundamental theorems namely, Slepian–Wolf and Wyner–Ziv. Recent research developments have made DVC attractive for applications in the emerging domain of wireless video sensor networks (WVSNs). This paper reviews the state-of-the-art DVC architectures with a focus on understanding their opportunities and gaps in addressing the operational requirements and application needs of WVSNs

    Nonlocal Myriad Filters for Cauchy Noise Removal

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    The contribution of this paper is two-fold. First, we introduce a generalized myriad filter, which is a method to compute the joint maximum likelihood estimator of the location and the scale parameter of the Cauchy distribution. Estimating only the location parameter is known as myriad filter. We propose an efficient algorithm to compute the generalized myriad filter and prove its convergence. Special cases of this algorithm result in the classical myriad filtering, respective an algorithm for estimating only the scale parameter. Based on an asymptotic analysis, we develop a second, even faster generalized myriad filtering technique. Second, we use our new approaches within a nonlocal, fully unsupervised method to denoise images corrupted by Cauchy noise. Special attention is paid to the determination of similar patches in noisy images. Numerical examples demonstrate the excellent performance of our algorithms which have moreover the advantage to be robust with respect to the parameter choice

    Locally adaptive image denoising by a statistical multiresolution criterion

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    We demonstrate how one can choose the smoothing parameter in image denoising by a statistical multiresolution criterion, both globally and locally. Using inhomogeneous diffusion and total variation regularization as examples for localized regularization schemes, we present an efficient method for locally adaptive image denoising. As expected, the smoothing parameter serves as an edge detector in this framework. Numerical examples illustrate the usefulness of our approach. We also present an application in confocal microscopy
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