The Basic Iterative Deconvolution: A Fast Instrumental Point-Spread Function Deconvolution Method That Corrects for Light That Is Scattered Out of the Field of View of a Detector

Abstract

A point-spread function describes the optics of an imaging system and can be used to correct collected images for instrumental effects. The state of the art for deconvolving images with the point-spread function is the Richardson–Lucy algorithm; however, despite its high fidelity, it is slow and cannot account for light scattered out of the field of view of the detector. We reinstate the Basic Iterative Deconvolution (BID) algorithm, a deconvolution algorithm that considers photons scattered out of the field of view of the detector, and extend it for image subregion deconvolutions. Its runtime is 1.8 to 7.1 faster than the Richardson–Lucy algorithm for \(4096 \times 4096\) pixel images and up to an additional factor of 150 for subregions of \(250 \times 250\) pixels. We test the extended BID algorithm for solar images taken by the Atmospheric Imaging Assembly (AIA), and find that the reconstructed intensities between BID and the Richardson–Lucy algorithm agree within 1%.