Low-Discrepancy Blue Noise Sampling

被引:31
|
作者
Ahmed, Abdalla G. M. [1 ]
Perrier, Helene [2 ]
Coeurjolly, David [2 ]
Ostromoukhov, Victor [2 ]
Guo, Jianwei [3 ]
Yan, Dong-Ming [3 ]
Huang, Hui [4 ,5 ]
Deussen, Oliver [1 ,5 ]
机构
[1] Univ Konstanz, Constance, Germany
[2] Univ Lyon, Lyon, France
[3] Chinese Acad Sci, Inst Automat, NLPR, Beijing, Peoples R China
[4] Shenzhen Univ, Shenzhen, Peoples R China
[5] SIAT, Shenzhen, Peoples R China
来源
ACM TRANSACTIONS ON GRAPHICS | 2016年 / 35卷 / 06期
基金
中国国家自然科学基金;
关键词
Blue Noise; Low Discrepancy; Sampling; Monte Carlo; quasi-Monte Carlo; WANG TILES; IMAGE;
D O I
10.1145/2980179.2980218
中图分类号
TP31 [计算机软件];
学科分类号
081202 ; 0835 ;
摘要
We present a novel technique that produces two-dimensional low-discrepancy (LD) blue noise point sets for sampling. Using one-dimensional binary van der Corput sequences, we construct two-dimensional LD point sets, and rearrange them to match a target spectral profile while preserving their low discrepancy. We store the rearrangement information in a compact lookup table that can be used to produce arbitrarily large point sets. We evaluate our technique and compare it to the state-of-the-art sampling approaches.
引用
收藏
页数:13
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