DESAT: an SSW tool for SDO/AIA image de-saturation

TL;DR

This paper introduces DESAT, a computational pipeline utilizing Expectation Maximization, correlation, and interpolation to effectively de-saturate images from SDO/AIA, improving image quality during solar flare events.

Contribution

The paper presents a novel de-saturation method and pipeline specifically designed for SDO/AIA images, combining mathematical techniques to enhance image clarity during saturation.

Findings

Effective de-saturation of SDO/AIA images demonstrated

Pipeline performs reliably on data from a 2014 solar flare

Computational analysis shows feasible processing demands

Abstract

Saturation affects a significant rate of images recorded by the Atmospheric Imaging Assembly on the Solar Dynamics Observatory. This paper describes a computational method and a technological pipeline for the de-saturation of such images, based on several mathematical ingredients like Expectation Maximization, image correlation and interpolation. An analysis of the computational properties and demands of the pipeline, together with an assessment of its reliability are performed against a set of data recorded from the Feburary 25 2014 flaring event.