Parallel object-oriented, denoising system using wavelet multiresolution analysis

Kamath, Chandrika; Baldwin, Chuck H.; Fodor, Imola K.; Tang, Nu A.

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Title: Parallel object-oriented, denoising system using wavelet multiresolution analysis

Abstract

The present invention provides a data de-noising system utilizing processors and wavelet denoising techniques. Data is read and displayed in different formats. The data is partitioned into regions and the regions are distributed onto the processors. Communication requirements are determined among the processors according to the wavelet denoising technique and the partitioning of the data. The data is transforming onto different multiresolution levels with the wavelet transform according to the wavelet denoising technique, the communication requirements, and the transformed data containing wavelet coefficients. The denoised data is then transformed into its original reading and displaying data format.

Inventors:: Kamath, Chandrika; Baldwin, Chuck H.; Fodor, Imola K.; Tang, Nu A.

Issue Date:: 2005-04-12

Research Org.:: The Regents of the University of California, Oakland, OH (United States); Lawrence Livermore National Laboratory (LLNL), Livermore, CA (United States)

Sponsoring Org.:: USDOE

OSTI Identifier:: 1175319

Patent Number(s):: 6879729

Application Number:: 09/877,962

Assignee:: The Regents of the University of California (Oakland, OH)

Patent Classifications (CPCs):: G - PHYSICS G06 - COMPUTING G06T - IMAGE DATA PROCESSING OR GENERATION, IN GENERAL

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G - PHYSICS G06 - COMPUTING G06T - IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
G06T2207/20064 - Wavelet transform [DWT]
G06T5/002 - {Denoising
G06T5/10 - by non-spatial domain filtering {

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DOE Contract Number:: W-7405-ENG-48

Resource Type:: Patent

Country of Publication:: United States

Language:: English

Subject:: 97 MATHEMATICS AND COMPUTING

Citation Formats


                    Kamath, Chandrika, Baldwin, Chuck H., Fodor, Imola K., and Tang, Nu A. Parallel object-oriented, denoising system using wavelet multiresolution analysis.  United States: N. p., 2005. 
        Web.

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                    Kamath, Chandrika, Baldwin, Chuck H., Fodor, Imola K., & Tang, Nu A. Parallel object-oriented, denoising system using wavelet multiresolution analysis.  United States.

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                    Kamath, Chandrika, Baldwin, Chuck H., Fodor, Imola K., and Tang, Nu A. Tue .  
        "Parallel object-oriented, denoising system using wavelet multiresolution analysis".  United States.  https://www.osti.gov/servlets/purl/1175319.

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@article{osti_1175319,

  title        = {Parallel object-oriented, denoising system using wavelet multiresolution analysis},

  author       = {Kamath, Chandrika and Baldwin, Chuck H. and Fodor, Imola K. and Tang, Nu A.},

  abstractNote = {The present invention provides a data de-noising system utilizing processors and wavelet denoising techniques. Data is read and displayed in different formats. The data is partitioned into regions and the regions are distributed onto the processors. Communication requirements are determined among the processors according to the wavelet denoising technique and the partitioning of the data. The data is transforming onto different multiresolution levels with the wavelet transform according to the wavelet denoising technique, the communication requirements, and the transformed data containing wavelet coefficients. The denoised data is then transformed into its original reading and displaying data format.},

  doi          = {},

  journal      = {},
number       = ,

  volume       = ,

  place        = {United States},

  year         = {2005},

  month        = {4}

}

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Works referenced in this record:

Design and implementation of a parallel object-oriented image processing toolkit
conference, October 2000

Kamath, Chandrika; Baldwin, Chuck H.; Fodor, Imola K.
International Symposium on Optical Science and Technology, SPIE Proceedings
https://doi.org/10.1117/12.403590

MLC++: a machine learning library in C++
conference, January 1994

Kohavi, R.; John, G.; Long, R.
Proceedings Sixth International Conference on Tools with Artificial Intelligence. TAI 94
https://doi.org/10.1109/TAI.1994.346412

Essential Wavelets for Statistical Applications and Data Analysis
book, January 1997

Ogden, R. Todd
https://doi.org/10.1007/978-1-4612-0709-2

Spatial adaptive wavelet thresholding for image denoising
conference, January 1997

Chang, S. G.; Vetterli, M.
Proceedings of International Conference on Image Processing
https://doi.org/10.1109/ICIP.1997.638782

WaveShrink: shrinkage functions and thresholds
conference, September 1995

Bruce, Andrew G.; Gao, Hong-Ye
SPIE's 1995 International Symposium on Optical Science, Engineering, and Instrumentation, SPIE Proceedings
https://doi.org/10.1117/12.217582

A System for Induction of Oblique Decision Trees
journal, August 1994

Murthy, S. K.; Kasif, S.; Salzberg, S.
Journal of Artificial Intelligence Research, Vol. 2
https://doi.org/10.1613/jair.63

Ideal spatial adaptation by wavelet shrinkage
journal, September 1994

Donoho, David L.; Johnstone, Iain M.
Biometrika, Vol. 81, Issue 3
https://doi.org/10.1093/biomet/81.3.425

ScalParC: a new scalable and efficient parallel classification algorithm for mining large datasets
conference, January 1998

Joshi, M. V.; Karypis, G.; Kumar, V.
Proceedings of the First Merged International Parallel Processing Symposium and Symposium on Parallel and Distributed Processing
https://doi.org/10.1109/IPPS.1998.669983

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Similar Records

Title: Parallel object-oriented, denoising system using wavelet multiresolution analysis

Abstract

Citation Formats

Design and implementation of a parallel object-oriented image processing toolkit conference, October 2000

MLC++: a machine learning library in C++ conference, January 1994

Essential Wavelets for Statistical Applications and Data Analysis book, January 1997

Spatial adaptive wavelet thresholding for image denoising conference, January 1997

WaveShrink: shrinkage functions and thresholds conference, September 1995

A System for Induction of Oblique Decision Trees journal, August 1994

Ideal spatial adaptation by wavelet shrinkage journal, September 1994

ScalParC: a new scalable and efficient parallel classification algorithm for mining large datasets conference, January 1998

Design and implementation of a parallel object-oriented image processing toolkit
conference, October 2000

MLC++: a machine learning library in C++
conference, January 1994

Essential Wavelets for Statistical Applications and Data Analysis
book, January 1997

Spatial adaptive wavelet thresholding for image denoising
conference, January 1997

WaveShrink: shrinkage functions and thresholds
conference, September 1995

A System for Induction of Oblique Decision Trees
journal, August 1994

Ideal spatial adaptation by wavelet shrinkage
journal, September 1994

ScalParC: a new scalable and efficient parallel classification algorithm for mining large datasets
conference, January 1998