SPICE PSF Correction: General Framework and Capability Demonstration

TL;DR

This paper introduces a novel sparse matrix solver-based method for correcting PSF artifacts in multidimensional data, capable of handling spatially varying PSFs and combining diverse instrument observations, demonstrated on Solar Orbiter SPICE data.

Contribution

The paper presents a new general framework for PSF correction using sparse matrix solvers, extending applicability beyond traditional deconvolution methods to spatially varying PSFs and multi-instrument data.

Findings

Effective correction of PSF artifacts in Solar Orbiter SPICE data

Applicable to spatially varying PSFs and multi-instrument datasets

Offers a robust alternative to deconvolution methods

Abstract

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