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dc.contributor.authorSuykens, Frank
dc.contributor.authorWillems, Yves D.
dc.contributor.editorSkala, Václav
dc.identifier.citationWSCG '2000: Conference proceeding: The 8th International Conference in Central Europe on Computers Graphics, Visualization and Interaktive Digital Media '2000 in cooperation with EUROGRAPHICS and IFIP WG 5.10: University of West Bohemia, Plzen, Czech republic, February 7 - 10, 2000, p. 220-227.en
dc.description.abstractImage filtering is often applied as a post-process to Monte Carlo generated pictures, in order to reduce noise. In this paper we present an algorithm based on density estimation techniques that applies an energy preserving adaptive kernel filter to individual samples during image rendering. The used kernel widths diminish as the number of samples goes up, ensuring a reasonable noise versus bias trade-off at any time. This results in a progressive algorithm, that still converges asymptotically to a correct solution. Results show that general noise as well as spike noise can effectively be reduced. Many interesting extensions are possible, making this a very promising technique for Monte Carlo image synthesis.en
dc.format8 s.cs
dc.publisherUniversity of West Bohemiaen
dc.relation.ispartofseriesWSCG '2000: Conference proceedingen
dc.rights© University of West Bohemiaen
dc.subjectglobální osvětlenícs
dc.subjectMonte Carlocs
dc.subjectodhad hustotycs
dc.subjecttrasování cestycs
dc.subjectfiltrování obrazucs
dc.titleAdaptive Filtering for Progressive Monte Carlo Image Renderingen
dc.typekonferenční příspěvekcs
dc.subject.translatedglobal illuminationen
dc.subject.translatedMonte Carloen
dc.subject.translateddensity estimationen
dc.subject.translatedpath tracingen
dc.subject.translatedimage filteringen
Appears in Collections:WSCG '2000: Conference proceeding

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Please use this identifier to cite or link to this item: http://hdl.handle.net/11025/15459

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