ASMOOTH: a simple and efficient algorithm for adaptive kernel smoothing of twodimensional imaging data
Abstract
An efficient algorithm for adaptive kernel smoothing (AKS) of twodimensional imaging data has been developed and implemented using the Interactive Data Language (IDL). The functional form of the kernel can be varied (tophat, Gaussian, etc.) to allow different weighting of the event counts registered within the smoothing region. For each individual pixel, the algorithm increases the smoothing scale until the signaltonoise ratio (S/N) within the kernel reaches a preset value. Thus, noise is suppressed very efficiently, while at the same time real structure, that is, signal that is locally significant at the selected S/N level, is preserved on all scales. In particular, extended features in noisedominated regions are visually enhanced. The ASMOOTH algorithm differs from other AKS routines in that it allows a quantitative assessment of the goodness of the local signal estimation by producing adaptively smoothed images in which all pixel values share the same S/N above the background.
We apply ASMOOTH to both real observational data (an Xray image of clusters of galaxies obtained with the Chandra Xray Observatory) and to a simulated data set. We find the ASMOOTHed images to be fair representations of the input data in the sense that the residuals are consistent with pure noise, that is, they possess Poissonian variance and a nearGaussian distribution around a mean of zero, and are spatially uncorrelated.
 Publication:

Monthly Notices of the Royal Astronomical Society
 Pub Date:
 May 2006
 DOI:
 10.1111/j.13652966.2006.10135.x
 arXiv:
 arXiv:astroph/0601306
 Bibcode:
 2006MNRAS.368...65E
 Keywords:

 methods: data analysis;
 methods: statistical;
 techniques: image processing;
 Astrophysics
 EPrint:
 9 pages, 5 figures, to be published in MNRAS