{"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, 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, 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}
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.
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).