title: Multi-Scale Gaussian Normalization for Solar Image Processing.
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 ofAtmospheric Imaging Assemblyon theSolar 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.
  
  
  BACKGROUND
  The online version of this article (doi:10.1007/s11207-014-0523-9) contains supplementary material, which is available to authorized users.
authors.affiliation: Sefydliad Mathemateg a Ffiseg, Prifysgol Aberystwyth, Ceredigion, SY23 3BZ Wales.
authors.forename: Huw
authors.initials: H
authors.lastname: Morgan
authors.affiliation: Faculty of Mechanical Engineering, Brno University of Technology, 616 69 Brno, Czech Republic.
authors.forename: Miloslav
authors.initials: M
authors.lastname: Druckmüller
keywords: Corona; Image processing