Detail publikace

Multi-Scale Gaussian Normalization for Solar Image Processing

MORGAN, H. DRUCKMÜLLER, M.

Originální název

Multi-Scale Gaussian Normalization for Solar Image Processing

Typ

článek v časopise ve Web of Science, Jimp

Jazyk

angličtina

Originální abstrakt

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.

Klíčová slova

Image processing; Corona

Autoři

MORGAN, H.; DRUCKMÜLLER, M.

Rok RIV

2014

Vydáno

1. 8. 2014

Nakladatel

Springer

ISSN

0038-0938

Periodikum

Solar Physics

Ročník

289

Číslo

8

Stát

Nizozemsko

Strany od

2945

Strany do

2955

Strany počet

11

URL

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

Plný text v Digitální knihovně

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"
}