research-article

Gabor noise by example

Authors:

Sylvain Lefebvre,

George DrettakisAuthors Info & Claims

ACM Transactions on Graphics (TOG), Volume 31, Issue 4

Article No.: 73, Pages 1 - 9

https://doi.org/10.1145/2185520.2185569

Published: 01 July 2012 Publication History

Abstract

Procedural noise is a fundamental tool in Computer Graphics. However, designing noise patterns is hard. In this paper, we present Gabor noise by example, a method to estimate the parameters of bandwidth-quantized Gabor noise, a procedural noise function that can generate noise with an arbitrary power spectrum, from exemplar Gaussian textures, a class of textures that is completely characterized by their power spectrum. More specifically, we introduce (i) bandwidth-quantized Gabor noise, a generalization of Gabor noise to arbitrary power spectra that enables robust parameter estimation and efficient procedural evaluation; (ii) a robust parameter estimation technique for quantized-bandwidth Gabor noise, that automatically decomposes the noisy power spectrum estimate of an exemplar into a sparse sum of Gaussians using non-negative basis pursuit denoising; and (iii) an efficient procedural evaluation scheme for bandwidth-quantized Gabor noise, that uses multi-grid evaluation and importance sampling of the kernel parameters. Gabor noise by example preserves the traditional advantages of procedural noise, including a compact representation and a fast on-the-fly evaluation, and is mathematically well-founded.

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cover image ACM Transactions on Graphics

ACM Transactions on Graphics Volume 31, Issue 4

July 2012

935 pages

ISSN:0730-0301

EISSN:1557-7368

DOI:10.1145/2185520

Issue’s Table of Contents

Copyright © 2012 ACM.

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Publication History

Published: 01 July 2012

Published in TOG Volume 31, Issue 4

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Moseva MKharrasov KGorodnichev M(2024)Neural Network Method for Removing the Effect of Atmospheric Noise in an Image2024 Systems of Signals Generating and Processing in the Field of on Board Communications10.1109/IEEECONF60226.2024.10496730(1-4)Online publication date: 12-Mar-2024
https://doi.org/10.1109/IEEECONF60226.2024.10496730
Wang SKo RBai GDong NChoi TZhang Y(2024)Evasion Attack and Defense on Machine Learning Models in Cyber-Physical Systems: A SurveyIEEE Communications Surveys & Tutorials10.1109/COMST.2023.334480826:2(930-966)Online publication date: Oct-2025
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