Electrical Engineering
      and Computer Sciences

Electrical Engineering and Computer Sciences

COLLEGE OF ENGINEERING

UC Berkeley

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