In particular, the volume of data provided by AIA/SDO is so high that low-resolution images are often used as a starting point to find features of interest. 2012), most scientific works begin from visual inspection of images. 2013).ĭespite the development of automated detection tools for EUV observations ( e.g. High-Resolution Coronal Imager (Hi-C), see Cirtain et al. Even as the community develop methods to digest the huge volume of data from AIA/SDO, new instruments are planned and tested with even finer resolution ( e.g. 2011) onboard the Solar Dynamics Observatory (SDO: Pesnell, Thompson, and Chamberlin 2012) provides very fine temporal and spatial resolution of the Sun at multiple wavelengths and is having a strong impact on the field. The Atmospheric Imaging Assembly (AIA: Lemen et al. As new EUV instruments are developed, the temporal, spatial, and spectral resolution becomes ever finer, giving new insight into the coronal and chromospheric structure and dynamics. We also applied the method successfully to a white-light coronagraph observation.Įxtreme ultra-violet (EUV) observations currently provide the most important source of information on the low solar corona. It also intrinsically flattens noisy regions and can reveal structure in off-limb regions out to the edge of the field of view. The method reveals information at the finest scales whilst maintaining enough of the larger-scale information to provide context. A very efficient process is described here, which is based on localised normalising of the data at many different spatial scales. 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. Processing of these images is important to reveal information, often hidden within the data, without introducing artefacts or bias. For even widths, the actual maximum of the array will be slightly less than the MAXIMUM value.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. If WIDTH is a vector, each element of WIDTH is used to specify the width for each dimension in the Gaussian. If WIDTH is a scalar, the same width is applied for every dimension. WIDTH can be either a scalar or vector value. This value can be used to override the value calculated from Sigma. If neither MAXIMUM nor NORMALIZE is set, the default maximum value will be set to 1.0. This keyword is ignored if MAXIMUM is set. If this keyword is set the peak height is calculated such that the Gaussian sum is 1.0.
If neither MAXIMUM or NORMALIZE is set, the default maximum value is set to 1.0. Set this keyword to the maximum value of the resulting array. Set this keyword to force the computations to be done in double-precision arithmetic. Unless the WIDTH keyword is set, the width of the kernel is determined by Sigma such that the kernel contains approximately three standard deviations in each dimension. Each element of Sigma is used to specify the sigma value for each dimension of the result.
The number of dimensions in the resulting kernel is equal to the number of elements in Sigma. Sigma can either be a scalar or a vector of up to eight elements. The sigma value used to calculate the Gaussian kernel. Result = GAUSSIAN_FUNCTION( Sigma ) Return Value Create a 2-D Gaussian with a sigma of 1 gauss1 = GAUSSIAN_FUNCTION() Create a 2-D Gaussian with a sigma of 3, a width of 20, and a peak height of 5 gauss2 = GAUSSIAN_FUNCTION(, WIDTH= 20, MAXIMUM= 5) Display the curves s1 = SURFACE(gauss1, LAYOUT=, STYLE= 1) s2 = SURFACE(gauss2, LAYOUT=, STYLE= 1, /CURRENT) Syntax
The GAUSSIAN_FUNCTION function creates a Gaussian kernel used in convolution.