@inproceedings{dd8ae05f3999474987af5af38806795c,
title = "AM-GM difference based adaptive sampling for Monte Carlo global illumination",
abstract = "Monte Carlo is the only choice for a physically correct method to do global illumination in the field of realistic image synthesis. Generally Monte Carlo based algorithms require a lot of time to eliminate the noise to get an acceptable image. Adaptive sampling is an interesting tool to reduce noise, in which the evaluation of homogeneity of pixel's samples is the key point. In this paper, we propose a new homogeneity measure, namely the arithmetic mean - geometric mean difference (abbreviated to AM - GM difference), which is developed to execute adaptive sampling efficiently. Implementation results demonstrate that our novel adaptive sampling method can perform significantly better than classic ones.",
keywords = "Adaptive sampling, Global illumination, Homogeneity measure, Monte Carlo",
author = "Qing Xu and Mateu Sbert and Miquel Feixas and Jianfeng Zhang",
year = "2007",
doi = "10.1007/978-3-540-74477-1\_56",
language = "English",
isbn = "9783540744757",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Verlag",
number = "PART 2",
pages = "602--611",
booktitle = "Computational Science and Its Applications - ICCSA 2007 - International Conference, Proceedings",
address = "Germany",
edition = "PART 2",
note = "International Conference on Computational Science and its Applications, ICCSA 2007 ; Conference date: 26-08-2007 Through 29-08-2007",
}