Deriving instrumental point spread functions from partially occulted images
Stefan Johann Hofmeister, Michael Hahn, Daniel Wolf Savin

TL;DR
This paper introduces a semi-empirical, semi-blind method to derive the point-spread function from partially occulted images, improving image correction and resolution without requiring predefined PSF models.
Contribution
A novel algorithm that accurately derives the PSF from various partially occulted images without assuming a specific functional form, applicable in diverse imaging contexts.
Findings
Central PSF weight accuracy better than 1.2%
Reconstruction error typically between 0.5% and 5%
Applicable to laboratory, astronomical, and eclipse images
Abstract
The point-spread function (PSF) of an imaging system describes the response of the system to a point source. Accurately determining the PSF enables one to correct for the combined effects of focussing and scattering within the imaging system, and thereby enhance the spatial resolution and dynamic contrast of the resulting images. We present a semi-empirical semi-blind methodology to derive a PSF from partially occulted images. We partition the two-dimensional PSF into multiple segments, set up a multi-linear system of equations, and directly fit the system of equations to determine the PSF weight in each segment. The algorithm is guaranteed to converge towards the correct instrumental PSF for a large class of occultations, does not require a predefined functional form of the PSF, and can be applied to a large variety of partially occulted images, such as within laboratory settings,…
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Taxonomy
TopicsAdvanced Image Processing Techniques · Adaptive optics and wavefront sensing · Meteorological Phenomena and Simulations
