Fast Filter Spreading and its Applications

Todd Jerome Kosloff, Justin Hensley and Brian A. Barsky

EECS Department
University of California, Berkeley
Technical Report No. UCB/EECS-2009-54
April 30, 2009

http://www.eecs.berkeley.edu/Pubs/TechRpts/2009/EECS-2009-54.pdf

In this paper, we introduce a technique called filter spreading, which provides a novel mechanism for filtering signals such as images. By using the repeated-integration technique of Heckbert, and the fast summed-area table construction technique of Hensley, we can implement fast filter spreading in real-time using current graphics processors. Our fast implementation of filter spreading is achieved by running the operations of the standard summed-area technique in reverse - e.g. instead of computing a summed-area table and then sampling from a table to generate the output, data is first placed in the table, and then an image is computed by taking the summed-area table of the generated table. While filter spreading with a spatially invariant kernel results in the same image as one produced using a traditional filter, by using a spatially varying filter kernel, our technique enables numerous interesting possibilities. (For example, filter spreading more naturally mimics the effects of real lenses, such as a limited depth of field.)


BibTeX citation:

@techreport{Kosloff:EECS-2009-54,
    Author = {Kosloff, Todd Jerome and Hensley, Justin and Barsky, Brian A.},
    Title = {Fast Filter Spreading and its Applications},
    Institution = {EECS Department, University of California, Berkeley},
    Year = {2009},
    Month = {Apr},
    URL = {http://www.eecs.berkeley.edu/Pubs/TechRpts/2009/EECS-2009-54.html},
    Number = {UCB/EECS-2009-54},
    Abstract = {In this paper, we introduce a technique called filter spreading, which provides a novel mechanism for filtering signals such as images. By using the repeated-integration technique of Heckbert, and the fast summed-area table construction technique of Hensley, we can implement fast filter spreading in real-time using current graphics processors. Our fast implementation of filter spreading is achieved by running the operations of the standard summed-area technique in reverse - e.g. instead of computing a summed-area table and then sampling from a table to generate the output, data is first placed in the table, and then an image is computed by taking the summed-area table of the generated table. While filter spreading with a spatially invariant kernel results in the same image as one produced using a traditional filter, by using a spatially varying filter kernel, our technique enables numerous interesting possibilities.  (For example, filter spreading more naturally mimics the effects of
real lenses, such as a limited depth of field.)}
}

EndNote citation:

%0 Report
%A Kosloff, Todd Jerome
%A Hensley, Justin
%A Barsky, Brian A.
%T Fast Filter Spreading and its Applications
%I EECS Department, University of California, Berkeley
%D 2009
%8 April 30
%@ UCB/EECS-2009-54
%U http://www.eecs.berkeley.edu/Pubs/TechRpts/2009/EECS-2009-54.html
%F Kosloff:EECS-2009-54