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

页数：13

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