Blue noise sampling with controlled aliasing
- 1 June 2013
- journal article
- research article
- Published by Association for Computing Machinery (ACM) in ACM Transactions on Graphics
- Vol. 32 (3) , 1-12
- https://doi.org/10.1145/2487228.2487233
Abstract
In this article we revisit the problem of blue noise sampling with a strong focus on the spectral properties of the sampling patterns. Starting from the observation that oscillations in the power spectrum of a sampling pattern can cause aliasing artifacts in the resulting images, we synthesize two new types of blue noise patterns: step blue noise with a power spectrum in the form of a step function and single-peak blue noise with a wide zero-region and no oscillations except for a single peak. We study the mathematical relationship of the radial power spectrum to a spatial statistic known as the radial distribution function to determine which power spectra can actually be realized and to construct the corresponding point sets. Finally, we show that both proposed sampling patterns effectively prevent structured aliasing at low sampling rates and perform well at high sampling rates.Keywords
This publication has 25 references indexed in Scilit:
- Differential domain analysis for non-uniform samplingACM Transactions on Graphics, 2011
- Blue-noise point sampling using kernel density modelACM Transactions on Graphics, 2011
- Physically valid statistical models for human motion generationACM Transactions on Graphics, 2011
- Electrostatic HalftoningComputer Graphics Forum, 2010
- Capacity-constrained point distributionsACM Transactions on Graphics, 2009
- Blue- and green-noise halftoning modelsIEEE Signal Processing Magazine, 2003
- Digital halftoning technique using a blue-noise maskJournal of the Optical Society of America A, 1992
- Spectrally optimal sampling for distribution ray tracingACM SIGGRAPH Computer Graphics, 1991
- Spectral Consequences of Photoreceptor Sampling in the Rhesus RetinaScience, 1983
- Least squares quantization in PCMIEEE Transactions on Information Theory, 1982