We present MOPEX - a software package for astronomical image processing and display. The package is a combination of command-line driven image processing software written in C/C++ with a Java-based GUI. The main image processing capabilities include creating mosaic images, image registration, background matching, point source extraction, as well as a number of minor image processing tasks. The combination of the image processing and display capabilities allows for much more intuitive and efficient way of performing image processing. The GUI allows for the control over the image processing and display to be closely intertwined. Parameter setting, validation, and specific processing options are entered by the user through a set of intuitive dialog boxes. Visualization feeds back into further processing by providing a prompt feedback of the processing results. The GUI also allows for further analysis by accessing and displaying data from existing image and catalog servers using a virtual observatory approach. Even though originally designed for the Spitzer Space Telescope mission, a lot of functionalities are of general usefulness and can be used for working with existing astronomical data and for new missions. The software used in the package has undergone intensive testing and benefited greatly from effective software reuse. The visualization part has been used for observation planning for both the Spitzer and Herschel Space Telescopes as part the tool Spot. The visualization capabilities of Spot have been enhanced and integrated with the image processing functionality of the command-line driven MOPEX. The image processing software is used in the Spitzer automated pipeline processing, which has been in operation for nearly 3 years. The image processing capabilities have also been tested in off-line processing by numerous astronomers at various institutions around the world. The package is multi-platform and includes automatic update capabilities. The software package has been developed by a small group of software developers and scientists at the Spitzer Science Center. It is available for distribution at the Spitzer Science Center web page.
We present MOPEX - a software package for mosaicking of astronomical images.MOPEX features image registration, background matching, usage of several interpolation techniques, coaddition schemes, and robust and flexible outlier detection based on spatial and temporal filtering. Image registration is based on matching the positions and fluxes of common point sources in image overlap regions. This information is used to compute a set of image offset corrections by globally minimizing the cumulative point source positional difference. A similar approach was used for background matching in overlapping. The cumulative pixel-by-pixel difference between the overlapping areas of all pairs of images is minimized with respect to the unknown constant offsets of the input images. The interpolation techniques used by MOPEX are the area overlap, drizzle, grid, and bicubic interpolation. We compare different interpolation techniques for their fidelity and speed. Robust outlier detection techniques allow for effective and reliable removal of the cosmic ray hits contaminating the detector array images. Efficient use of computer memory allows mosaicking of data sets of very deep coverage of thousands of images per pointing, as well as areas of sky covering many square degrees. MOPEX has been developed for the Spitzer Space Telescope.
Point Response Function (PRF) is an important characteristic of the combination of an optical system and the detector array. It has various applications, such as accurate photometry and astrometry, image interpolation and deconvolution. We present a technique of PRF estimation for undersampled detectors. We present the results of application of this technique to the data taken by the Infrared Array Camera (IRAC) of the Spitzer Space Telescope. The technique capitalizes on the numerous observations of point sources that cover the whole detector array as well as the area of an individual pixel. Data fitting is used to center the point source images on the sub-pixel level and to normalize them by the point source flux. They are subsequently resampled and shifted to a common grid using bicubic interpolation. Great redundancy of the data allows for effective outlier rejection. The quality of PRF estimation is verified using simulated images and real images taken by the Spitzer Space Telescope.
A new nonlinear diffusion filtering scheme based on a nonlinear diffusion equation with a variable scale parameter is developed to preserve faint point sources while smoothing images for segmentation purposes. Application of the proposed approach to simulated, as well as to real images obtained by the Spitzer Space Telescope and by the Chandra X-ray Observatory reduced the Gaussian and Poisson noise successfully, while preserving both point sources and diffuse structures.
The task of object detection depends on the ability to suppress the noise present in images in order to increase the signal-to-noise ratio. The standard linear matched filter is the optimal filter on the assumption of the Gaussian distribution of the signal and the noise. However, as a rule the distribution of the signal in image processing is not Gaussian. The linear matched filter becomes sub-optimal. Any non-Gaussian distribution function can be closely approximated using the Gaussian Mixture Model (GMM). We use GMM to approximate the signal distribution function and derive the optimal filter by means of mean square error (MSE) minimization. The optimal non-linear filter is determined by the assumed signal distribution function. We use non-linear matched filtering for point source detection in astronomical images. We derive the GMM components by fitting the theoretical point source distribution function. The filtered images are subjected to image segmentation and subsequent point source detection. The non-linear matched filtering has been tested with simulated data and has been shown to significantly improve the quality of point source detection. Receiver operating characteristic technique has been used to evaluate performance of various Gaussian mixtures for point source detection. This algorithm is currently used for the Spitzer Spatial Telescope.
The Spitzer Space Telescope Infrared Array Camera (IRAC) is a four-channel camera that uses two pairs of 256 x 256 pixel InSb and Si:As IBC detectors to provide simultaneous images at 3.6, 4.5, 5.8, and 8 microns. IRAC experiences a flux of cosmic rays that produce transient events in images from each of the arrays, with 5-7 pixels per second being affected in an IRAC integration. The vast majority of these transient events can be adequately characterized so they can be effectively detected and flagged by a pipeline software module. However, because of the nature of the arrays and their arrangement in the camera structure, a small fraction of the cosmic ray hits on IRAC produce transients with unusual morphologies which cannot be characterized in a general way. We present nominal cosmic ray rates observed for IRAC on-orbit and rates observed during a period of elevated solar proton flux following a series of X-class solar flares in late 2003. We also present a guide for observers to help identify unusual transient events in their data. We comment on the physical nature of the production of many o9f these unusual transients and how this mechanism differs from the production of "normal" transient events.