{"title":"Total Variation (TV) l1 Norm Minimization Based Limited Data X-ray CT Image Reconstruction","authors":"S. Sarkar, P. Wahi, P. Munshi","doi":"10.1080/09349847.2019.1673857","DOIUrl":null,"url":null,"abstract":"ABSTRACT Limited data CT Image reconstruction is a real-life problem. A Total Variation (TV) l1 norm minimization technique has been examined and validated here to reconstruct CT images from limited data incorporating a limited number of views along with limited angular span, a situation typical in engineering applications. The Lagrangian technique has been used to solve TV equations. The reconstructed CT image has been compared with the images reconstructed by SIRT, Higher Order TV (HOTV) technique, l2 norm minimization based technique and some other techniques with the help of various image quality index (IQI) parameters. The comparison shows that the proposed scheme is an attractive solution for limited data CT Image reconstruction from industrial and engineering perspective. The application of Sobolev space error analysis has also been given to ensure good global reconstruction.","PeriodicalId":54493,"journal":{"name":"Research in Nondestructive Evaluation","volume":"1 1","pages":"164 - 186"},"PeriodicalIF":1.0000,"publicationDate":"2020-05-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Research in Nondestructive Evaluation","FirstCategoryId":"88","ListUrlMain":"https://doi.org/10.1080/09349847.2019.1673857","RegionNum":4,"RegionCategory":"材料科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"MATERIALS SCIENCE, CHARACTERIZATION & TESTING","Score":null,"Total":0}
引用次数: 2
Abstract
ABSTRACT Limited data CT Image reconstruction is a real-life problem. A Total Variation (TV) l1 norm minimization technique has been examined and validated here to reconstruct CT images from limited data incorporating a limited number of views along with limited angular span, a situation typical in engineering applications. The Lagrangian technique has been used to solve TV equations. The reconstructed CT image has been compared with the images reconstructed by SIRT, Higher Order TV (HOTV) technique, l2 norm minimization based technique and some other techniques with the help of various image quality index (IQI) parameters. The comparison shows that the proposed scheme is an attractive solution for limited data CT Image reconstruction from industrial and engineering perspective. The application of Sobolev space error analysis has also been given to ensure good global reconstruction.
期刊介绍：
Research in Nondestructive Evaluation® is the archival research journal of the American Society for Nondestructive Testing, Inc. RNDE® contains the results of original research in all areas of nondestructive evaluation (NDE). The journal covers experimental and theoretical investigations dealing with the scientific and engineering bases of NDE, its measurement and methodology, and a wide range of applications to materials and structures that relate to the entire life cycle, from manufacture to use and retirement.
Illustrative topics include advances in the underlying science of acoustic, thermal, electrical, magnetic, optical and ionizing radiation techniques and their applications to NDE problems. These problems include the nondestructive characterization of a wide variety of material properties and their degradation in service, nonintrusive sensors for monitoring manufacturing and materials processes, new techniques and combinations of techniques for detecting and characterizing hidden discontinuities and distributed damage in materials, standardization concepts and quantitative approaches for advanced NDE techniques, and long-term continuous monitoring of structures and assemblies. Of particular interest is research which elucidates how to evaluate the effects of imperfect material condition, as quantified by nondestructive measurement, on the functional performance.
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