Compressive estimation for signal integration in rendering

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dc.contributor.authorSen, Pradeepen_US
dc.contributor.authorDarabi, Soheilen_US
dc.date.accessioned2015-02-23T16:57:41Z
dc.date.available2015-02-23T16:57:41Z
dc.date.issued2010en_US
dc.description.abstractIn rendering applications, we are often faced with the problem of computing the integral of an unknown function. Typical approaches used to estimate these integrals are often based on Monte Carlo methods that slowly converge to the correct answer after many point samples have been taken. In this work, we study this problem under the framework of compressed sensing and reach the conclusion that if the signal is sparse in a transform domain, we can evaluate the integral accurately using a small set of point samples without requiring the lengthy iterations of Monte Carlo approaches. We demonstrate the usefulness of our framework by proposing novel algorithms to address two problems in computer graphics: image antialiasing and motion blur. We show that we can use our framework to generate good results with fewer samples than is possible with traditional approaches.en_US
dc.description.number4en_US
dc.description.seriesinformationComputer Graphics Forumen_US
dc.description.volume29en_US
dc.identifier.doi10.1111/j.1467-8659.2010.01731.xen_US
dc.identifier.issn1467-8659en_US
dc.identifier.pages1355-1363en_US
dc.identifier.urihttps://doi.org/10.1111/j.1467-8659.2010.01731.xen_US
dc.publisherThe Eurographics Association and Blackwell Publishing Ltden_US
dc.titleCompressive estimation for signal integration in renderingen_US
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29-Issue 4
EGSR10: 21th Eurographics Symposium on Rendering