Denoising observational data

TL;DR

This paper reviews noise reduction techniques in observational data, introduces a non-local averaging method applied to solar magnetograms, and emphasizes preserving resolution and minimizing artefacts for improved scientific analysis.

Contribution

It presents a non-local averaging denoising method tailored for observational data, demonstrating its effectiveness on solar magnetograms and G-band data.

Findings

Effective noise reduction while preserving resolution

Minimal introduction of artefacts in processed data

Applicable to various observational data types

Abstract

Reducing noise caused by the instrumentation in observational data is a crucial step in data post-processing. A method is searched for that conserves most of the instrumental resolution and introduces as few methodical artefacts as possible. With such a method integrated in an observation sites software tool-chain, the resources spent for the generation of observational data will more likely find their way into resulting scientific publications; otherwise, for data post-processing often methods are used, which just smear out the noise, introduce artefacts, or decrease the provided resolution in space or time. A short review of different techniques is given here, and a non-local averaging method is applied to Hinode magnetograms and G-band data. The presented method fits the needs for various kinds of observational data.