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Parallel Image Reconstruction Using the Maximum Likelihood Method with a Graphics Processor and the OpenGL Library.

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Title: Parallel Image Reconstruction Using the Maximum Likelihood Method with a Graphics Processor and the OpenGL Library.
Authors: Zolotarev, S. A.1 (AUTHOR) sergei.zolotarev@gmail.com, Taruat, A. T.2 (AUTHOR) ahmedtharwat6773@gmail.com
Source: Russian Journal of Nondestructive Testing. Jun2024, Vol. 60 Issue 6, p648-657. 10p.
Subjects: Image reconstruction, Maximum likelihood statistics, Texture mapping, Graphics processing units, Aluminum castings, Expectation-maximization algorithms, Parallel algorithms
Abstract: Creating fast parallel iterative statistical algorithms based on the use of graphics accelerators is an important and urgent task of great scientific and practical importance. An algorithm based on the method of maximizing the maximum likelihood expectation (maximum likelihood expectation maximization—MLEM) is considered. The MLEM is a numerical method for determining maximum likelihood estimates and, since its first application in the field of image reconstruction in 1982, remains one of the most popular statistical image reconstruction methods and is the foundation for many other approaches. A new version of the MLEM parallel algorithm is proposed that provides global convergence of the iterative algorithm. To parallelize the algorithm, we use the texture mapping method using the OpenGL graphics library. The parallel algorithm is described in as much detail as possible. Examples of several reconstructions of images of aluminum casting products are given. The obtained result can be used for nondestructive testing of various industrial products, including testing of foundry products. [ABSTRACT FROM AUTHOR]
Copyright of Russian Journal of Nondestructive Testing is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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  Data: Parallel Image Reconstruction Using the Maximum Likelihood Method with a Graphics Processor and the OpenGL Library.
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  Data: <searchLink fieldCode="JN" term="%22Russian+Journal+of+Nondestructive+Testing%22">Russian Journal of Nondestructive Testing</searchLink>. Jun2024, Vol. 60 Issue 6, p648-657. 10p.
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  Data: <searchLink fieldCode="DE" term="%22Image+reconstruction%22">Image reconstruction</searchLink><br /><searchLink fieldCode="DE" term="%22Maximum+likelihood+statistics%22">Maximum likelihood statistics</searchLink><br /><searchLink fieldCode="DE" term="%22Texture+mapping%22">Texture mapping</searchLink><br /><searchLink fieldCode="DE" term="%22Graphics+processing+units%22">Graphics processing units</searchLink><br /><searchLink fieldCode="DE" term="%22Aluminum+castings%22">Aluminum castings</searchLink><br /><searchLink fieldCode="DE" term="%22Expectation-maximization+algorithms%22">Expectation-maximization algorithms</searchLink><br /><searchLink fieldCode="DE" term="%22Parallel+algorithms%22">Parallel algorithms</searchLink>
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  Label: Abstract
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  Data: Creating fast parallel iterative statistical algorithms based on the use of graphics accelerators is an important and urgent task of great scientific and practical importance. An algorithm based on the method of maximizing the maximum likelihood expectation (maximum likelihood expectation maximization—MLEM) is considered. The MLEM is a numerical method for determining maximum likelihood estimates and, since its first application in the field of image reconstruction in 1982, remains one of the most popular statistical image reconstruction methods and is the foundation for many other approaches. A new version of the MLEM parallel algorithm is proposed that provides global convergence of the iterative algorithm. To parallelize the algorithm, we use the texture mapping method using the OpenGL graphics library. The parallel algorithm is described in as much detail as possible. Examples of several reconstructions of images of aluminum casting products are given. The obtained result can be used for nondestructive testing of various industrial products, including testing of foundry products. [ABSTRACT FROM AUTHOR]
– Name: AbstractSuppliedCopyright
  Label:
  Group: Ab
  Data: <i>Copyright of Russian Journal of Nondestructive Testing is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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        Value: 10.1134/S1061830924700682
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      – Code: eng
        Text: English
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        PageCount: 10
        StartPage: 648
    Subjects:
      – SubjectFull: Image reconstruction
        Type: general
      – SubjectFull: Maximum likelihood statistics
        Type: general
      – SubjectFull: Texture mapping
        Type: general
      – SubjectFull: Graphics processing units
        Type: general
      – SubjectFull: Aluminum castings
        Type: general
      – SubjectFull: Expectation-maximization algorithms
        Type: general
      – SubjectFull: Parallel algorithms
        Type: general
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      – TitleFull: Parallel Image Reconstruction Using the Maximum Likelihood Method with a Graphics Processor and the OpenGL Library.
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            – D: 01
              M: 06
              Text: Jun2024
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              Y: 2024
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