Super-Drizzle: Applications of Adaptive Kernel Regression in Astronomical Imaging

摘要

The drizzle algorithm is a widely used tool for image enhancement in the astronomical literature. For example, a very popular implementation of this method, as studied by Frutcher and Hook, has been used to fuse, denoise, and increase the spatial resolution of the images captured by the Hubble Space Telescope (HST). However, the drizzle algorithm is an ad-hoc method, equivalent to a spatially adaptive linear filter, which limits its range of performance. To improve the performance of the drizzle algorithm, we make contact with the field of non-parametric statistics and generalize the tools and results for use in image processing and reconstruction. In contrast to the parametric methods, which rely on a specific model of the signal of interest, non-parametric methods rely on the data itself to dictate the structure of the model, in which case this implicit model is referred to as a regression function. We promote the use and improve upon a class of non-parametric methods called kernel regression.