A fast tunable blurring algorithm for scattered data
This addresses the problem of efficient blurring for scattered spatial data, which is incremental as it extends existing methods to non-uniform grids.
The paper introduces a fast tunable blurring algorithm for scattered data that generalizes conventional blurring to non-uniform grids, enabling applications like deblurring and scale separation. An example shows its use in decomposing oceanographic float measurements into large-scale and small-scale components.
A blurring algorithm with linear time complexity can reduce the small-scale content of data observed at scattered locations in a spatially extended domain of arbitrary dimension. The method works by forming a Gaussian interpolant of the input data, and then convolving the interpolant with a multiresolution Gaussian approximation of the Green's function to a differential operator whose spectrum can be tuned for problem-specific considerations. Like conventional blurring algorithms, which the new algorithm generalizes to data measured at locations other than a uniform grid, applications include deblurring and separation of spatial scales. An example illustrates a possible application toward enabling importance sampling approaches to data assimilation of geophysical observations, which are often scattered over a spatial domain, since blurring observations can make particle filters more effective at state estimation of large scales. Another example, motivated by data analysis of dynamics like ocean eddies that have strong separation of spatial scales, uses the algorithm to decompose scattered oceanographic float measurements into large-scale and small-scale components.