To exploit uncertainty masking for adaptive image rendering

Lu Dong, Weisi Lin, Chenwei Deng, Ce Zhu, Hock Soon Seah

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

For high-quality image rendering using Monte Carlo methods, a large number of samples are required to be computed for each pixel. Adaptive sampling aims to decrease the total number of samples by concentrating samples on difficult regions. However, existing adaptive sampling schemes haven't fully exploited the potential of image regions with complex structures to the reduction of sample numbers. To solve this problem, we propose to exploit uncertainty masking in adaptive sampling. Experimental results show that incorporation of uncertainty information leads to significant sample reduction and therefore time-savings.

Original languageEnglish
Title of host publication2013 IEEE International Symposium on Circuits and Systems, ISCAS 2013
Pages2848-2851
Number of pages4
DOIs
Publication statusPublished - 2013
Event2013 IEEE International Symposium on Circuits and Systems, ISCAS 2013 - Beijing, China
Duration: 19 May 201323 May 2013

Publication series

NameProceedings - IEEE International Symposium on Circuits and Systems
ISSN (Print)0271-4310

Conference

Conference2013 IEEE International Symposium on Circuits and Systems, ISCAS 2013
Country/TerritoryChina
CityBeijing
Period19/05/1323/05/13

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