Publication detail

Multi-Scale Gaussian Normalization for Solar Image Processing

MORGAN, H. DRUCKMÜLLER, M.

Original Title

Multi-Scale Gaussian Normalization for Solar Image Processing

Type

journal article in Web of Science

Language

English

Original Abstract

Extreme ultra-violet images of the corona contain information over a wide range of spatial scales, and different structures such as active regions, quiet Sun, and filament channels contain information at very different brightness regimes. Processing of these images is important to reveal information, often hidden within the data, without introducing artefacts or bias. It is also important that any process be computationally efficient, particularly given the fine spatial and temporal resolution of Atmospheric Imaging Assembly on the Solar Dynamics Observatory (AIA/SDO), and consideration of future higher resolution observations. A very efficient process is described here, which is based on localised normalising of the data at many different spatial scales. The method reveals information at the finest scales whilst maintaining enough of the larger-scale information to provide context. It also intrinsically flattens noisy regions and can reveal structure in off-limb regions out to the edge of the field of view. We also applied the method successfully to a white-light coronagraph observation.

Keywords

Image processing; Corona

Authors

MORGAN, H.; DRUCKMÜLLER, M.

RIV year

2014

Released

1. 8. 2014

Publisher

Springer

ISBN

0038-0938

Periodical

Solar Physics

Year of study

289

Number

8

State

Kingdom of the Netherlands

Pages from

2945

Pages to

2955

Pages count

11

URL

https://link.springer.com/article/10.1007%2Fs11207-014-0523-9

Full text in the Digital Library

http://hdl.handle.net/11012/201728

BibTex

@article{BUT109713,
  author="Huw {Morgan} and Miloslav {Druckmüller}",
  title="Multi-Scale Gaussian Normalization for Solar Image Processing",
  journal="Solar Physics",
  year="2014",
  volume="289",
  number="8",
  pages="2945--2955",
  doi="10.1007/s11207-014-0523-9",
  issn="0038-0938",
  url="https://link.springer.com/article/10.1007%2Fs11207-014-0523-9"
}