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

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 [1], 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 [2, 3].