A New Method for Band-limited Imaging with Undersampled Detectors

Abstract

Since its original use on the Hubble Deep Field, "Drizzle" has become a de facto standard for the combination of images taken by the Hubble Space Telescope. However, the Drizzle algorithm was developed with small, faint, partially resolved sources in mind and is not the best possible algorithm for unresolved objects with high signal-to-noise ratios. Here, a new method for creating band-limited images from undersampled data is presented. The method uses a drizzled image as a first-order approximation and then rapidly converges toward a band-limited image that fits the data, given the statistical weighting provided by the drizzled image. The method, named iDrizzle, for iterative Drizzle, effectively eliminates both the small high-frequency artifacts and convolution with an interpolant kernel that can be introduced by drizzling. The method works well in the presence of geometric distortion and can easily handle cosmic rays, bad pixels, or other missing data. It can combine images taken with random dithers, though the number of dithers required to obtain a good final image depends, in part, on the quality of the dither placements. iDrizzle may prove most beneficial for producing high-fidelity point-spread functions from undersampled images and could be particularly valuable for future dark energy missions such as Wide-Field Infrared Survey Telescope and Euclid, which will likely attempt to do high-precision supernova photometry and lensing experiments with undersampled detectors.