Fast Filter Spreading and its Applications
Todd Jerome Kosloff and Justin Hensley and Brian A. Barsky
EECS Department, University of California, Berkeley
Technical Report No. UCB/EECS-2009-54
April 30, 2009
http://www2.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}, Year= {2009}, Month= {Apr}, Url= {http://www2.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://www2.eecs.berkeley.edu/Pubs/TechRpts/2009/EECS-2009-54.html %F Kosloff:EECS-2009-54