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Few‐view image reconstruction combining total variation and a high‐order norm
Author(s) -
Zhang Yi,
Zhang WeiHua,
Chen Hu,
Yang MengLong,
Li TaiYong,
Zhou JiLiu
Publication year - 2013
Publication title -
international journal of imaging systems and technology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.359
H-Index - 47
eISSN - 1098-1098
pISSN - 0899-9457
DOI - 10.1002/ima.22058
Subject(s) - norm (philosophy) , computer science , maximization , minification , regular polygon , projection (relational algebra) , iterative reconstruction , algorithm , square root , convex optimization , variation (astronomy) , image (mathematics) , mathematical optimization , mean squared error , mathematics , artificial intelligence , geometry , statistics , physics , political science , astrophysics , law
This work presents a novel computed tomography reconstruction method for few‐view problem based on a compound method. To overcome the disadvantages of total variation (TV) minimization method, we use a high‐order norm coupled within TV and the numerical scheme for our method is given. We use the root mean square error as a referee. The numerical experiments demonstrate that our method achieves better performance than existing reconstruction methods, including filtered back projection, expectation maximization, and TV with projection on convex sets. © 2013 Wiley Periodicals, Inc. Int J Imaging Syst Technol, 23, 249–255, 2013