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J.C. O'Linger (JPL), J.W. Fowler, R.R. Laher (Caltech/IPAC), T. Herter (Cornell)
The WIRE mission will conduct a deep infrared survey to examine the evolution of starburst galaxies and search for protogalaxies. The survey-data quality will depend primarily on the object density in the field of view and the telescope resolving power. Source confusion, even in the deepest survey images, may significantly hinder our ability to interpret the data. The Maximum Correlation Method (MCM) of image processing can potentially enhance the quality of the final WIRE image products beyond simple coaddition of suitably-processed survey images. MCM is an image construction algorithm which can create an image with spatial information beyond what is contained in a single instrumental image but implicit in a collection of them. Unlike standard deconvolution methods, MCM does not assume a constant point-spread function. Under ideal conditions, i.e., near-optimal sampling and high signal-to-noise, the resolution of the constructed image can approach or even exceed the diffraction limit of the optics. The chief limiting factor is the amount of information available from the raw survey data and the accuracy of the observational model. MCM was used with great success in the HIRES algorithm in the IRAS data processing program, YORIC, and has demonstrated the ability to improve the effective resolution of the instrument significantly at all four IRAS wavebands. Preliminary tests with simulated raw WIRE images show improvements over results from traditional image coaddition of registered, resampled WIRE frames. This project is relevant to future infrared data handling, data reduction, and image processing. Our goal is to advance infrared image processing techniques, not only in preparation for the upcoming WIRE mission, but for other missions, including SIRTF.
The author(s) of this abstract have provided an email address for comments about the abstract: joanno@ipac.caltech.edu