使用图形处理器和 OpenGL 库以最大似然法并行重建图像

IF 0.9 4区 材料科学 Q4 MATERIALS SCIENCE, CHARACTERIZATION & TESTING Russian Journal of Nondestructive Testing Pub Date : 2024-09-23 DOI:10.1134/S1061830924700682
S. A. Zolotarev, A. T. Taruat
{"title":"使用图形处理器和 OpenGL 库以最大似然法并行重建图像","authors":"S. A. Zolotarev,&nbsp;A. T. Taruat","doi":"10.1134/S1061830924700682","DOIUrl":null,"url":null,"abstract":"<p>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.</p>","PeriodicalId":764,"journal":{"name":"Russian Journal of Nondestructive Testing","volume":null,"pages":null},"PeriodicalIF":0.9000,"publicationDate":"2024-09-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Parallel Image Reconstruction Using the Maximum Likelihood Method with a Graphics Processor and the OpenGL Library\",\"authors\":\"S. A. Zolotarev,&nbsp;A. T. Taruat\",\"doi\":\"10.1134/S1061830924700682\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p>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.</p>\",\"PeriodicalId\":764,\"journal\":{\"name\":\"Russian Journal of Nondestructive Testing\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":0.9000,\"publicationDate\":\"2024-09-23\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Russian Journal of Nondestructive Testing\",\"FirstCategoryId\":\"88\",\"ListUrlMain\":\"https://link.springer.com/article/10.1134/S1061830924700682\",\"RegionNum\":4,\"RegionCategory\":\"材料科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q4\",\"JCRName\":\"MATERIALS SCIENCE, CHARACTERIZATION & TESTING\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Russian Journal of Nondestructive Testing","FirstCategoryId":"88","ListUrlMain":"https://link.springer.com/article/10.1134/S1061830924700682","RegionNum":4,"RegionCategory":"材料科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q4","JCRName":"MATERIALS SCIENCE, CHARACTERIZATION & TESTING","Score":null,"Total":0}
引用次数: 0

摘要

利用图形加速器创建快速并行迭代统计算法是一项重要而紧迫的任务,具有重大的科学和现实意义。我们考虑了一种基于最大似然期望最大化方法(最大似然期望最大化-MLEM)的算法。MLEM 是一种确定最大似然估计值的数值方法,自 1982 年首次应用于图像重建领域以来,一直是最流行的统计图像重建方法之一,也是许多其他方法的基础。本文提出了一种新版本的 MLEM 并行算法,可实现迭代算法的全局收敛。为了实现算法的并行化,我们使用了 OpenGL 图形库的纹理映射方法。我们尽可能详细地描述了并行算法。我们给出了几个铝铸造产品图像重建的例子。获得的结果可用于各种工业产品的无损检测,包括铸造产品的检测。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

摘要图片

查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
Parallel Image Reconstruction Using the Maximum Likelihood Method with a Graphics Processor and the OpenGL Library

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.

求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
Russian Journal of Nondestructive Testing
Russian Journal of Nondestructive Testing 工程技术-材料科学:表征与测试
CiteScore
1.60
自引率
44.40%
发文量
59
审稿时长
6-12 weeks
期刊介绍: Russian Journal of Nondestructive Testing, a translation of Defectoskopiya, is a publication of the Russian Academy of Sciences. This publication offers current Russian research on the theory and technology of nondestructive testing of materials and components. It describes laboratory and industrial investigations of devices and instrumentation and provides reviews of new equipment developed for series manufacture. Articles cover all physical methods of nondestructive testing, including magnetic and electrical; ultrasonic; X-ray and Y-ray; capillary; liquid (color luminescence), and radio (for materials of low conductivity).
期刊最新文献
Erratum to: Analysis of Weak Signal Detection Based on Tri-Stable System under Poisson White Noise Nondestructive Detection of Wire Rope Damage Using Leakage Magnetic Technique based on Dual-Layer Sensors Erratum to: Solid Particle Erosion Behaviour of Laser Sintered Heat Treated Ti–6Al–4V Alloy Enhanced Electromagnetic Near Field Probe for Diagnosis and Materials Characterization Some Cases of Explicit Expression of the Intensity of the Resulting Field of Magnets Placed in the Field of External Sources
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 联系我们:info@booksci.cn
Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。
Copyright © 2023 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1