We describe a new iteration method to estimate asteroid coordinates, which is based on the subpixel Gaussian model of a discrete object image. The method operates by continuous parameters (asteroid coordinates) in a discrete observational space (the set of pixels potential) of the CCD frame. In this model, a kind of the coordinate distribution of the photons hitting a pixel of the CCD frame is known a priori, while the associated parameters are determined from a real digital object image. The developed method, being more flexible in adapting to any form of the object image, has a high measurement accuracy along with a low calculating complexity due to a maximum likelihood procedure, which is implemented to obtain the best fit instead of a least-squares method and Levenberg-Marquardt algorithm for the minimisation of the quadratic form. 
Since 2010, the method was tested as the basis of our CoLiTec (Collection Light Technology) software, which has been installed at several observatories of the world with the aim of automatic discoveries of asteroids and comets on a set of CCD frames. As the result, four comets (C/2010 X1 (Elenin), P/2011 NO1(Elenin), C/2012 S1 (ISON), and P/2013 V3 (Nevski)) as well as more than 1500 small Solar System bodies (including five NEOs, 21 Trojan asteroids of Jupiter, and one Centaur object) were discovered. We discuss these results that allowed us to compare the accuracy parameters of a new method and confirm its efficiency. 
In 2014, the CoLiTec software was recommended to all members of the Gaia-FUN-SSO network for analysing observations as a tool to detect faint moving objects in frames.

A new method based on the subpixel Gaussian model for accurate estimation of asteroid coordinates

A concept of a new software architecture for astronomical image processing is presented. Current approaches of astronomical data extraction are explained: photometry, astrometry and high-resolution imaging. The methods seem incommensurate with each other and suffer from systematic errors caused by known issues. Hints are given, where and how to consolidate these algorithms into a more sophisticated approach to improve data retrieval from images. A detailed discussion of user experience and possible expectations is presented. The analysis yielded current astronomical image processing software based on a shell oriented task collection designed and implemented since the 80ies. From a historical perspective, requirements for any next generation software design can be identified as backward compatibility, platform independence, extensibility, a more intuitive user experience, documentation issues, and long-term reliability. A smooth software transition is proposed. Object-oriented and XML driven approaches may help to combine the existing software packages and future extensions within a common user interface. A unified, formal task description language for image processing is proposed to support generality.

Astroinformatics - A Study about Constraints and Requirements for Next Generation Astronomical Image Processing

This dissertation addresses the components necessary for simulation of an image-based recovery of the position of a target using orbital image sensors. Each component is considered in detail, focusing on the effect that design choices and system parameters have on the accuracy of the position estimate. Changes in sensor resolution, varying amounts of blur, differences in image noise level, selection of algorithms used for each component, and lag introduced by excessive processing time all contribute to the accuracy of the result regarding recovery of target coordinates using orbital sensor data. Using physical targets and sensors in this scenario would be cost-prohibitive in the exploratory setting posed, therefore a simulated target path is generated using Bezier curves which approximate representative paths followed by the targets of interest. Orbital trajectories for the sensors are designed on an elliptical model representative of the motion of physical orbital sensors. Images from each sensor are simulated based on the position and orientation of the sensor, the position of the target, and the imaging parameters selected for the experiment (resolution, noise level, blur level, etc.). Post-processing of the simulated imagery seeks to reduce noise and blur and increase resolution. The only information available for calculating the target position by a fully implemented system are the sensor position and orientation vectors and the images from each sensor. From these data we develop a reliable method of recovering the target position and analyze the impact on near-realtime processing. We also discuss the influence of adjustments to system components on overall capabilities and address the potential system size, weight, and power requirements from realistic implementation approaches.

Computational Imaging Approach to Recovery of Target Coordinates Using Orbital Sensor Data

Astronomy and geosciences are deeply related and converging fields. Both industries process large volumes of spatially enabled data in similar ways, with commonalities in remote sensing techniques, coordinate transformations, distance measurement, analysis of time series, and spectroscopy. Yet the two industries employ different software and standards, hindering interoperability. Virtual European Solar and Planetary Access (VESPA) is an initiative to bring together the virtual observatory and the planetary science through the development of common tools, interfaces, and data access protocols. With deeper integration, geosciences can benefit from the adoption of data discovery methods and distributed data storage techniques developed by the virtual observatory, while astronomy would be able to leverage on the extensive data processing and web mapping tools developed by the geosciences.

Synergy in Astronomy and Geosciences

The key features of the MATPHOT algorithm for precise and accurate stellar photometry and astrometry using discrete Point Spread Functions are described. A discrete Point Spread Function (PSF) is a sampled version of a continuous PSF which describes the two-dimensional probability distribution of photons from a point source (star) just above the detector. The shape information about the photon scattering pattern of a discrete PSF is typically encoded using a numerical table (matrix) or a FITS image file. Discrete PSFs are shifted within an observational model using a 21-pixel-wide damped sinc function and position partial derivatives are computed using a five-point numerical differentiation formula. Precise and accurate stellar photometry and astrometry is achieved with undersampled CCD observations by using supersampled discrete PSFs that are sampled 2, 3, or more times more finely than the observational data. The precision and accuracy of the MATPHOT algorithm is demonstrated by using the C-language MPD code to analyze simulated CCD stellar observations; measured performance is compared with a theoretical performance model. Detailed analysis of simulated Next Generation Space Telescope observations demonstrate that millipixel relative astrometry and millimag photometric precision is achievable with complicated space-based discrete PSFs. For further information about MATPHOT and MPD, including source code and documentation, see this http URL

Stellar Photometry and Astrometry with Discrete Point Spread Functions

The determination of accurate positions of stars is an essential task to obtain a reference coordinate system for precise navigation of ships, airplanes, satellites and space crafts. Stellar positions and proper motions will also help to investigate the structure and evolution of matter in the universe. From astronomical observations systematic errors have been reported by different authors using different techniques to detect and measure positions of point-light sources from digital images. An overview of this problem of image processing is given. The relative subpixel deviation will be defined and described. It provides interesting characteristics independent from the method of position retrieval or point spread introduced by any image deterioration. The relative subpixel deviation defines the lower limit of the achievable precision of positions. The function is the error correction term by itself, which yields a new concept of the error correction. A concept of a new simulation software is presented to further investigate the relative subpixel deviation. Features of the core framework are: support of different optical systems, sources of noise, and detectors types, like monochrome or color CCD and CMOS imagers, their pixel geometry, gaps and varying subpixel sensitivity functions. First results from the simulator software as well as results from new astronomical observations are presented as a proof of concept of the proposed approach to improve the current limit of position accuracy in the order of a few 1/100 of a pixel.

Improving the Accuracy of Position Detection of Point Light Sources on Digital Images

Super-Resolution Imaging: The Use Case of Optical Astronomy

Efficient Pixel Binning of Photographs

This work describes the application of digital single lens reflex (SLR) cameras in astronomy science. A definition of the term raw file format is given. As with formats like FITS or TIFF, the CR2 file format is explained beeing a disclosed, and well documented raw file format. Results from raw image file decomposition with different software tools, and a comparison of imaging with a digital SLR camera and a professional grade CCD detector are presented. The analysis of noise and image histograms will help to recover problems with raw file format decomposition tools and their adjustment. Without doubt, digital SLR cameras are suited for special scientific applications. Further implications from this work are a detailed analysis of noise characteristics to achieve high quality imaging at very low light levels from raw files taken with digital SLR cameras.

The RAW Image File Format Problem - Applications of Digital SLR Cameras in Astronomy and Science.

Efficient Binning of Photographs

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