Non-parametric PSF estimation from celestial transit solar images using blind deconvolution

Context: Characterization of instrumental effects in astronomical imaging is important in order to extract accurate physical information from the observations. The measured image in a real optical instrument is usually represented by the convolution of an ideal image with a Point Spread Function (PSF). Additionally, the image acquisition process is also contaminated by other sources of noise (read-out, photon-counting). The problem of estimating both the PSF and a denoised image is called blind deconvolution and is ill-posed. Aims: We propose a blind deconvolution scheme that relies on image regularization. Contrarily to most methods presented in the literature, our method does not assume a parametric model of the PSF and can thus be applied to any telescope. Methods: Our scheme uses a wavelet analysis prior model on the image and weak assumptions on the PSF. We use observations from a celestial transit, where the occulting body can be assumed to be a black disk. These constraints allow us to retain meaningful solutions for the filter and the image, eliminating trivial, translated and interchanged solutions. Under an additive Gaussian noise assumption, they also enforce noise canceling and avoid reconstruction artifacts by promoting the whiteness of the residual between the blurred observations and the cleaned data. Results: Our method is applied to synthetic and experimental data. The PSF is estimated for the SECCHI/EUVI instrument using the 2007 Lunar transit, and for SDO/AIA using the 2012 Venus transit. Results show that the proposed non-parametric blind deconvolution method is able to estimate the core of the PSF with a similar quality to parametric methods proposed in the literature. We also show that, if these parametric estimations are incorporated in the acquisition model, the resulting PSF outperforms both the parametric and non-parametric methods.Comment: 31 pages, 47 figure

Quantifying and containing the curse of high resolution coronal imaging

Future missions such as Solar Orbiter (SO), InterHelioprobe, or Solar Probe aim at approaching the Sun closer than ever before, with on board some high resolution imagers (HRI) having a subsecond cadence and a pixel area of about $(80km)^2$ at the Sun during perihelion. In order to guarantee their scientific success, it is necessary to evaluate if the photon counts available at these resolution and cadence will provide a sufficient signal-to-noise ratio (SNR). We perform a first step in this direction by analyzing and characterizing the spatial intermittency of Quiet Sun images thanks to a multifractal analysis. We identify the parameters that specify the scale-invariance behavior. This identification allows next to select a family of multifractal processes, namely the Compound Poisson Cascades, that can synthesize artificial images having some of the scale-invariance properties observed on the recorded images. The prevalence of self-similarity in Quiet Sun coronal images makes it relevant to study the ratio between the SNR present at SoHO/EIT images and in coarsened images. SoHO/EIT images thus play the role of 'high resolution' images, whereas the 'low-resolution' coarsened images are rebinned so as to simulate a smaller angular resolution and/or a larger distance to the Sun. For a fixed difference in angular resolution and in Spacecraft-Sun distance, we determine the proportion of pixels having a SNR preserved at high resolution given a particular increase in effective area. If scale-invariance continues to prevail at smaller scales, the conclusion reached with SoHO/EIT images can be transposed to the situation where the resolution is increased from SoHO/EIT to SO/HRI resolution at perihelion.Comment: 25 pages, 1 table, 7 figure

Segmentation d'Images solaires en Extrême Ultraviolet par une Approche Classification floue Multispectrale

L'étude de la variabilité de la couronne solaire et le suivi de régions caractéristiques à sa surface (régions actives, trous coronaux) sont d'une importance capitale en astrophysique et pour le développement de la météorologie de l'espace. Dans ce cadre, nous proposons un algorithme de segmentation multispectrale d'images du Soleil acquises en extrême ultraviolet, utilisant un algorithme de classification flou spatialement contraint. L'utilisation de la logique floue permet de prendre en compte les imprécisions et les incertitudes inhérentes à la définition des différentes régions d'intérêt dans l'image. La méthode est appliquée sur des images prises par le téléscope EIT du satellite SoHO, depuis janvier 1997 jusque mai 2005, couvrant ainsi presque l'intégralité d'un cycle solaire. Les résultats en terme de caractérisation géométrique et radiométrique des régions actives et des trous coronaux sont en accord avec d'autres observations menées par ailleurs. La méthode met de plus en évidence des périodes dans la série temporelle étudiée, reliées à des phénomènes de physique solaire connus

Virtual Super Resolution of Scale Invariant Textured Images Using Multifractal Stochastic Processes

International audienceWe present a new method of magnification for textured images featuring scale invariance properties. This work is originally motivated by an application to astronomical images. One goal is to propose a method to quantitatively predict statistical and visual properties of images taken by a forthcoming higher resolution telescope from older images at lower resolution. This is done by performing a virtual super resolution using a family of scale invariant stochastic processes, namely compound Poisson cascades, and fractional integration. The procedure preserves the visual aspect as well as the statistical properties of the initial image. An augmentation of information is performed by locally adding random small scale details below the initial pixel size. This extrapolation procedure yields a potentially infinite number of magnified versions of an image. It allows for large magnification factors (virtually infinite) and is physically conservative: zooming out to the initial resolution yields the initial image back. The (virtually) super resolved images can be used to predict the quality of future observations as well as to develop and test compression or denoising techniques

Amélioration virtuelle de la résolution d'images du Soleil par augmentation d'information invariante d'échelle

4 pagesNational audienceCurrent images of the quiet Sun from the spatial telescope EIT are such that 1 pixel = (1800km)2 whereas the smallest physical scales would be of about 100 m. The design of a high resolution spatial telescope where 1 pixel = (80 km)2 is planned. With a resolution 25 times finer, the images may be under-exposed or even useless. The point is to predict at best the quality of these images from the current observations. We exploit the scale invariance properties of images currently available to suggest a method to artificially improve (of a potentially infinite factor) the current images resolution by integrating details from a multifractal stochastic model. Quiet Sun images are magnified by a factor 32 while preserving the multiscale properties (spectrum, multiscaling) and assuring that reducing the magnified image gives the initial image back. We deduce from that an extrapolation of histograms of high resolution images allowing a prediction of the quality of images from a future high resolution telescope

Vers un modèle sous-pixel des images de Soleil calme dans l'extrême ultra-violet

Nous nous intéressons à la modélisation d'images du Soleil acquises dans l'extrême ultraviolet par le télescope Extreme ultraviolet Imaging Telescope (EIT) de la mission Solar and Heliospheric Observatory (SoHO, ESA/NASA). Nous nous intéressons aux régions les moins structurées en apparence, le "Soleil calme". Nous présentons d'abord une analyse multifractale des images de Soleil calme. Au-delà de l'analyse des données, il s'agit d'identifier un modèle stochastique des images étudiées à partir duquel il sera possible de simuler des images similaires mais de résolution arbitrairement fine en exploitant la propriété d'invariance d'échelle. Nous comparons deux familles de modèles (cascades infiniment divisibles et draps stables fractionnaires) permettant de simuler numériquement des images statistiquement similaires aux images de Soleil calme. Cette modélisation permettra la préparation des prochaines observations à haute résolution et d'étudier la variabilité sous-pixel des images du Soleil

Improvements on coronal hole detection in SDO/AIA images using supervised classification

We demonstrate the use of machine learning algorithms in combination with segmentation techniques in order to distinguish coronal holes and filaments in SDO/AIA EUV images of the Sun. Based on two coronal hole detection techniques (intensity-based thresholding, SPoCA), we prepared data sets of manually labeled coronal hole and filament channel regions present on the Sun during the time range 2011 - 2013. By mapping the extracted regions from EUV observations onto HMI line-of-sight magnetograms we also include their magnetic characteristics. We computed shape measures from the segmented binary maps as well as first order and second order texture statistics from the segmented regions in the EUV images and magnetograms. These attributes were used for data mining investigations to identify the most performant rule to differentiate between coronal holes and filament channels. We applied several classifiers, namely Support Vector Machine, Linear Support Vector Machine, Decision Tree, and Random Forest and found that all classification rules achieve good results in general, with linear SVM providing the best performances (with a true skill statistic of ~0.90). Additional information from magnetic field data systematically improves the performance across all four classifiers for the SPoCA detection. Since the calculation is inexpensive in computing time, this approach is well suited for applications on real-time data. This study demonstrates how a machine learning approach may help improve upon an unsupervised feature extraction method.Comment: in press for SWS

Nonparametric monitoring of sunspot number observations: a case study

Solar activity is an important driver of long-term climate trends and must be accounted for in climate models. Unfortunately, direct measurements of this quantity over long periods do not exist. The only observation related to solar activity whose records reach back to the seventeenth century are sunspots. Surprisingly, determining the number of sunspots consistently over time has remained until today a challenging statistical problem. It arises from the need of consolidating data from multiple observing stations around the world in a context of low signal-to-noise ratios, non-stationarity, missing data, non-standard distributions and many kinds of errors. The data from some stations experience therefore severe and various deviations over time. In this paper, we propose the first systematic and thorough statistical approach for monitoring these complex and important series. It consists of three steps essential for successful treatment of the data: smoothing on multiple timescales, monitoring using block bootstrap calibrated CUSUM charts and classifying of out-of-control situations by support vector techniques. This approach allows us to detect a wide range of anomalies (such as sudden jumps or more progressive drifts), unseen in previous analyses. It helps us to identify the causes of major deviations, which are often observer or equipment related. Their detection and identification will contribute to improve future observations. Their elimination or correction in past data will lead to a more precise reconstruction of the world reference index for solar activity: the International Sunspot Number.Comment: 27 pages (without appendices), 6 figure

Segmentation of extreme ultraviolet solar images via multichannel fuzzy clustering

International audienceThe study of the variability of the solar corona and the monitoring of its traditional regions (Coronal Holes, Quiet Sun and Active Regions) are of great importance in astrophysics as well as in view of the Space Weather and Space Climate applications. Here we propose a multichannel unsupervised spatially constrained fuzzy clustering algorithm that automatically segments EUV solar images into Coronal Holes, Quiet Sun and Active Regions. Fuzzy logic allows to manage the various noises present in the images and the imprecision in the definition of the above regions. The process is fast and automatic. It is applied to SoHO–EIT images taken from February 1997 till May 2005, i.e. along almost a full solar cycle. Results in terms of areas and intensity estimations are consistent with previous knowledge. The method reveal the rotational and other mid-term periodicities in the extracted time series across solar cycle 23. Further, such an approach paves the way to bridging observations between spatially resolved data from imaging telescopes and time series from radiometers. Time series resulting form the segmentation of EUV coronal images can indeed provide an essential component in the process of reconstructing the solar spectrum