We present here a novel concept for achieving real-time super-resolution ground-based imagery for small aperture telescopes. We explore the combination of existing stacking and registration software in conjunction with real-time equation based Data Models. Our research indicates that for anisoplanatic imagery, a real-time video/software enhanced analog to conventional speckle imaging is possible. This paper highlights the technique and theory for creating such a system.
We present a method for simulating CCD focal plane array (FPA) images of extended deep sky objects using Data Modeling. Data Modeling is a process of deriving functional equations from measured data. These tools are used to model FPA fixed pattern noise, shot noise, non-uniformity, and the extended objects themselves. The mathematical model of the extended object is useful for correlation analysis and other image understanding algorithms used in Virtual Observatory Data Mining. We apply these tools to the objects in the Messier list and build a classifier that achieves 100% correct classification.
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We propose a novel approach for index-tagging Virtual Observatory data files with descriptive statistics enabling rapid data mining and mathematical modeling. This is achieved by calculating at data collection time 6 standard moments as descriptive file tags. Data Change Detection Models are derived from these tags and used to filter databases for similar or dissimilar information such as stellar spectra, photometric data, images, and text. Currently, no consistent or reliable method for searching, collating, and comparing 2-D imagery exists. Traditionally, methods used to address these data problems are disparate and unrelated to text data mining and extraction. We explore the use of mathematical Data Models as a unifying tool set for enabling data mining across all data class domains.