Documentation/Nightly/Modules/AstroSmoothing

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Introduction and Acknowledgements

Authors: Davide Punzo (Kapteyn Astronomical Institute, University of Groningen) Thijs van der Hulst (Kapteyn Astronomical Institute, University of Groningen) Jos Roerdink (Johann Bernoulli Institute for Mathematics and Computer Science, University of Groningen) Contributors: Steve Pieper (Isomics) Jean-Christophe Fillion-Robin (Kitware) Ken Martin (Kitware) Acknowledgements: This work was supported by the European Research Council under the European Union's Seventh Framework Programme (FP/2007-2013)/ERC Grant Agreement nr. 291-531. Contacts: Davide Punzo, <email>punzodavide@hotmail.it</email>; <email>D.Punzo@astro.rug.nl</email> Thijs van der Hulst, <email>J.M.van.der.Hulst@astro.rug.nl</email> Jos Roerdink <email>j.b.t.m.roerdink@rug.nl</email> Back to SlicerAstro home

Module Description

The AstroSmoothing module contains a set of filters aimed to denoise astronomical (HI) dataset:

• Anisotropic Box;
• Anisotropic Gaussian;
• Intensity-driven Gradient.

These algorithms are available in SlicerAstro as parallelized implementations on both CPU and GPU hardware, offering interactive performance when processing data-cubes of dimensions up to 10⁷ voxels and very fast performance (< 3.5 sec) for larger ones (up to 10⁸ voxels).

The intensity-driven gradient filter, due to its adaptive characteristics, is the optimal choice for HI data (see [Punzo2016](https://arxiv.org/pdf/1609.03782.pdf)). Therefore, it is the default method when the automatic mode has been chosen. This algorithm preserves the detailed structure of the signal with high signal-to-noise ratio (> 3) at the highest resolution, while smoothing only the faint part of the signal (signal-to-noise ratio < 3). For more information regarding the filters and their performance, default parameters, advantages and disadvantages, we refer to [Punzo2016](https://arxiv.org/pdf/1609.03782.pdf).

Panels and their use

A comparative layout of the output generated by the AstroSmoothing module is shown. The interface includes a widget for changing the input parameters for the smoothing and a table showing the output segmentations generated after the smoothing process. This includes the widgets for changing the input (such as the filter choice, the computational hardware and the smoothing parameters) and visualizing the output segmentation objects generated by the smoothing process. The comparative layout is composed of two 3-D views and three 2-D views. In the left 3-D view and the 2-D views the data are shown. In the right 3-D view the filtered version of the data is shown. The data are rendered in different colors that highlight the data at different intensity levels: green, blue and red correspond to 3, 7 and 15 times the rms noise respectively. The colored segmentations represent masks automatically calculated by the filtering algorithm. The light blue and yellow segmentations (visualized as contour plots in the 2-D views) are a 3 rms thresholding of the input data and the filtered data, respectively.

References

@ARTICLE{2016A&C....17..163P,
   author = {{Punzo}, D. and {van der Hulst}, J.~M. and {Roerdink}, J.~B.~T.~M.
	},
    title = "{Finding faint H I structure in and around galaxies: Scraping the barrel}",
  journal = {Astronomy and Computing},
archivePrefix = "arXiv",
   eprint = {1609.03782},
 primaryClass = "astro-ph.IM",
 keywords = {Radio lines: galaxies, Techniques: image processing, Scientific visualization},
     year = 2016,
    month = oct,
   volume = 17,
    pages = {163-176},
      doi = {10.1016/j.ascom.2016.09.002},
}