Multi-source data fusion and super-resolution from astronomical images
| Autor: | E. Slezak, J. A. Gutiérrez, André Jalobeanu |
|---|---|
| Přispěvatelé: | Laboratoire de Cosmologie, Astrophysique Stellaire & Solaire, de Planétologie et de Mécanique des Fluides (CASSIOPEE), Université Nice Sophia Antipolis (... - 2019) (UNS), COMUE Université Côte d'Azur (2015-2019) (COMUE UCA)-COMUE Université Côte d'Azur (2015-2019) (COMUE UCA)-Institut national des sciences de l'Univers (INSU - CNRS)-Observatoire de la Côte d'Azur, Université Côte d'Azur (UCA)-COMUE Université Côte d'Azur (2015-2019) (COMUE UCA)-Université Côte d'Azur (UCA)-Centre National de la Recherche Scientifique (CNRS) |
| Rok vydání: | 2008 |
| Předmět: |
Statistics and Probability
Image formation Orientation (computer vision) Model selection 020206 networking & telecommunications 02 engineering and technology Sensor fusion Bayesian inference computer.software_genre Data set Redundancy (information theory) 0202 electrical engineering electronic engineering information engineering A priori and a posteriori 020201 artificial intelligence & image processing Data mining computer Mathematics |
| Zdroj: | Statistical Methodology Statistical Methodology, Elsevier, 2008, 5, pp.361-372. ⟨10.1016/j.stamet.2008.02.002⟩ |
| ISSN: | 1572-3127 |
| DOI: | 10.1016/j.stamet.2008.02.002 |
| Popis: | Virtual observatories give us access to huge amounts of image data that are often redundant. Our goal is to take advantage of this redundancy by combining images of the same field of view into a single model. To achieve this goal, we propose to develop a multi-source data fusion method that relies on probability and band-limited signal theory. The target object is an image to be inferred from a number of blurred and noisy sources, possibly from different sensors under various conditions (i.e. resolution, shift, orientation, blur, noise...). We aim at the recovery of a compound model “image + uncertainties” that best relates to the observations and contains a maximum of useful information from the initial data set. Thus, in some cases, spatial super-resolution may be required in order to preserve the information. We propose to use a Bayesian inference scheme to invert a forward model, which describes the image formation process for each observation and takes into account some a priori knowledge (e.g. stars as point sources). This involves both automatic registration and spatial resampling, which are ill-posed inverse problems that are addressed within a rigorous Bayesian framework. The originality of the work is in devising a new technique of multi-image data fusion that provides us with super-resolution, self-calibration and possibly model selection capabilities. This approach should outperform existing methods such as resample-and-add or drizzling since it can handle different instrument characteristics for each input image and compute uncertainty estimates as well. Moreover, it is designed to also work in a recursive way, so that the model can be updated when new data become available. |
| Databáze: | OpenAIRE |
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