Real-Time Virtual Environment Signal Extraction and Denoising Using Programmable Graphics Hardware

Research output: Contribution to journalArticle

Abstract

The sense of being within a three-dimensional (3D) space and interacting with virtual 3D objects in a computer-generated virtual environment (VE) often requires essential image, vision and sensor signal processing techniques such as differentiating and denoising. This paper describes novel implementations of the Gaussian filtering for characteristic signal extraction and wavelet-based image denoising algorithms that run on the graphics processing unit (GPU). While significant acceleration over standard CPU implementations is obtained through exploiting data parallelism provided by the modern programmable graphics hardware, the CPU can be freed up to run other computations more efficiently such as artificial intelligence (AI) and physics. The proposed GPU-based Gaussian filtering can extract surface information from a real object and provide its material features for rendering and illumination. The wavelet-based signal denoising for large size digital images realized in this project provided better realism for VE visualization without sacrificing real-time and interactive performances of an application.

Original languageEnglish
Pages (from-to)326-334
Number of pages9
JournalInternational Journal of Automation and Computing
Volume6
Issue number4
DOIs
Publication statusPublished - Oct 2009

Fingerprint

Signal Extraction
Graphics Hardware
Graphics Processing Unit
Denoising
Virtual Environments
Virtual reality
Program processors
Wavelets
Filtering
Signal denoising
Data Parallelism
Real-time
Hardware
Image denoising
Image Denoising
Digital Image
Rendering
Computer hardware
Artificial intelligence
Signal Processing

Cite this

@article{e783ce86a30a4d9386f6e52f75562047,
title = "Real-Time Virtual Environment Signal Extraction and Denoising Using Programmable Graphics Hardware",
abstract = "The sense of being within a three-dimensional (3D) space and interacting with virtual 3D objects in a computer-generated virtual environment (VE) often requires essential image, vision and sensor signal processing techniques such as differentiating and denoising. This paper describes novel implementations of the Gaussian filtering for characteristic signal extraction and wavelet-based image denoising algorithms that run on the graphics processing unit (GPU). While significant acceleration over standard CPU implementations is obtained through exploiting data parallelism provided by the modern programmable graphics hardware, the CPU can be freed up to run other computations more efficiently such as artificial intelligence (AI) and physics. The proposed GPU-based Gaussian filtering can extract surface information from a real object and provide its material features for rendering and illumination. The wavelet-based signal denoising for large size digital images realized in this project provided better realism for VE visualization without sacrificing real-time and interactive performances of an application.",
keywords = "GPU-Based Gaussian filtering, Graphics processing unit, Signal denoising, Virtual environment, Wavelet",
author = "Xu, {Zhi Jie} and Jiang, {Xiang Qian} and Yang Su",
year = "2009",
month = "10",
doi = "10.1007/s11633-009-0326-x",
language = "English",
volume = "6",
pages = "326--334",
journal = "International Journal of Automation and Computing",
issn = "1476-8186",
publisher = "Chinese Academy of Sciences",
number = "4",

}

TY - JOUR

T1 - Real-Time Virtual Environment Signal Extraction and Denoising Using Programmable Graphics Hardware

AU - Xu, Zhi Jie

AU - Jiang, Xiang Qian

AU - Su, Yang

PY - 2009/10

Y1 - 2009/10

N2 - The sense of being within a three-dimensional (3D) space and interacting with virtual 3D objects in a computer-generated virtual environment (VE) often requires essential image, vision and sensor signal processing techniques such as differentiating and denoising. This paper describes novel implementations of the Gaussian filtering for characteristic signal extraction and wavelet-based image denoising algorithms that run on the graphics processing unit (GPU). While significant acceleration over standard CPU implementations is obtained through exploiting data parallelism provided by the modern programmable graphics hardware, the CPU can be freed up to run other computations more efficiently such as artificial intelligence (AI) and physics. The proposed GPU-based Gaussian filtering can extract surface information from a real object and provide its material features for rendering and illumination. The wavelet-based signal denoising for large size digital images realized in this project provided better realism for VE visualization without sacrificing real-time and interactive performances of an application.

AB - The sense of being within a three-dimensional (3D) space and interacting with virtual 3D objects in a computer-generated virtual environment (VE) often requires essential image, vision and sensor signal processing techniques such as differentiating and denoising. This paper describes novel implementations of the Gaussian filtering for characteristic signal extraction and wavelet-based image denoising algorithms that run on the graphics processing unit (GPU). While significant acceleration over standard CPU implementations is obtained through exploiting data parallelism provided by the modern programmable graphics hardware, the CPU can be freed up to run other computations more efficiently such as artificial intelligence (AI) and physics. The proposed GPU-based Gaussian filtering can extract surface information from a real object and provide its material features for rendering and illumination. The wavelet-based signal denoising for large size digital images realized in this project provided better realism for VE visualization without sacrificing real-time and interactive performances of an application.

KW - GPU-Based Gaussian filtering

KW - Graphics processing unit

KW - Signal denoising

KW - Virtual environment

KW - Wavelet

UR - http://www.scopus.com/inward/record.url?scp=70350560520&partnerID=8YFLogxK

U2 - 10.1007/s11633-009-0326-x

DO - 10.1007/s11633-009-0326-x

M3 - Article

VL - 6

SP - 326

EP - 334

JO - International Journal of Automation and Computing

JF - International Journal of Automation and Computing

SN - 1476-8186

IS - 4

ER -