SPICE point spread function correction: General framework and capability demonstration

Abstract

We present a new method of removing point spread function (PSF) artifacts and improving the resolution of multidimensional data sources, including imagers and spectrographs. Rather than deconvolution, which is translationally invariant, the method we present is based on sparse matrix solvers. This allows it to be applied to spatially varying PSFs as well as to combined observations from instruments with radically different spatial, spectral, or thermal response functions (e.g., SDO/AIA and RHESSI). The method was developed to correct PSF artifacts in Solar Orbiter Spectral Imaging of the Coronal Environment, so the motivation, presentation of the method, and the results revolve around this type of application. However, it can be used as a more robust (e.g., with respect to spatially varying PSFs) alternative to deconvolution of 2D image data, as well as similar problems, and is also relevant to more general linear inversion problems.