Multi-Scale Gaussian Normalization for Solar Image Processing Multi-Scale Gaussian Normalization for Solar Image Processing Multi-Scale Gaussian Normalization for Solar Image Processing

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216305%3A26210%2F14%3APU110653" target="_blank" >RIV/00216305:26210/14:PU110653 - isvavai.cz</a>

Výsledek na webu

<a href="http://dx.doi.org/10.1007/s11207-014-0523-9" target="_blank" >http://dx.doi.org/10.1007/s11207-014-0523-9</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1007/s11207-014-0523-9" target="_blank" >10.1007/s11207-014-0523-9</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Multi-Scale Gaussian Normalization for Solar Image Processing Multi-Scale Gaussian Normalization for Solar Image Processing Multi-Scale Gaussian Normalization for Solar Image Processing

Popis výsledku v původním jazyce

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.

Název v anglickém jazyce

Multi-Scale Gaussian Normalization for Solar Image Processing Multi-Scale Gaussian Normalization for Solar Image Processing Multi-Scale Gaussian Normalization for Solar Image Processing

Popis výsledku anglicky

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.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

10308 - Astronomy (including astrophysics,space science)

Projekt

—

Návaznosti

S - Specificky vyzkum na vysokych skolach

Rok uplatnění

2014

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název periodika

Solar Physics

ISSN

0038-0938

e-ISSN

1573-093X

Svazek periodika

2014 (289)

Číslo periodika v rámci svazku

8

Stát vydavatele periodika

NL - Nizozemsko

Počet stran výsledku

11

Strana od-do

2945-2955

Kód UT WoS článku

000336334900008

EID výsledku v databázi Scopus

2-s2.0-84901315291