9 research outputs found
Image Coaddition with Temporally Varying Kernels
Large, multi-frequency imaging surveys, such as the Large Synaptic Survey
Telescope (LSST), need to do near-real time analysis of very large datasets.
This raises a host of statistical and computational problems where standard
methods do not work. In this paper, we study a proposed method for combining
stacks of images into a single summary image, sometimes referred to as a
template. This task is commonly referred to as image coaddition. In part, we
focus on a method proposed in previous work, which outlines a procedure for
combining stacks of images in an online fashion in the Fourier domain. We
evaluate this method by comparing it to two straightforward methods through the
use of various criteria and simulations. Note that the goal is not to propose
these comparison methods for use in their own right, but to ensure that
additional complexity also provides substantially improved performance