Multi-Scale Gaussian Normalization for Solar Image Processing

Huw Morgan, Miloslav Druckmuller

Research output: Contribution to journalArticlepeer-review

156 Citations (SciVal)
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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.
Original languageEnglish
Pages (from-to)2945-2955
Number of pages11
JournalSolar Physics
Volume289
Issue number8
Early online date08 Apr 2014
DOIs
Publication statusPublished - 31 Aug 2014

Keywords

  • image processing
  • Corona

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