X-Rays Tomographic Reconstruction Images using Proximal Methods based on L1 Norm and TV Regularization

被引:5
作者
Allag, Aicha [1 ,2 ]
Drai, Redouane [1 ]
Benammar, Abdessalem [1 ]
Boutkedjirt, Tarek [2 ]
机构
[1] Res Ctr Ind Technol CRTI, POB 64, Algiers 16014, Algeria
[2] Univ Sci & Technol Houari Boumediene, POB 32,DZ 6111, Algiers, Algeria
来源
PROCEEDINGS OF THE FIRST INTERNATIONAL CONFERENCE ON INTELLIGENT COMPUTING IN DATA SCIENCES (ICDS2017) | 2018年 / 127卷
关键词
X-ray tomographic; total variation; proximal methods; ALGORITHM;
D O I
10.1016/j.procs.2018.01.119
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
In this paper, sparse regularization methods are applied to X-rays tomographic reconstruction 2D images. These methods are based on total variation algorithm associated to L(1)norm and proximal functions. The inverse problem can therefore be regularized by using total variation regularisation based on proximal functions such as Forward-Backward, Douglas-Rachford and Chambolle-Pock approaches. We applied this method to non-destructive evaluation of material in the case of 2D reconstruction of X-rays tomographic images containing real defects. (c) 2018 The Authors. Published by Elsevier B.V.
引用
收藏
页码:236 / 245
页数:10
相关论文
共 22 条
[11]   An iterative thresholding algorithm for linear inverse problems with a sparsity constraint [J].
Daubechies, I ;
Defrise, M ;
De Mol, C .
COMMUNICATIONS ON PURE AND APPLIED MATHEMATICS, 2004, 57 (11) :1413-1457
[12]   Compressed sensing [J].
Donoho, DL .
IEEE TRANSACTIONS ON INFORMATION THEORY, 2006, 52 (04) :1289-1306
[13]   A Proximal Iteration for Deconvolving Poisson Noisy Images Using Sparse Representations [J].
Dupe, Francois-Xavier ;
Fadili, Jalal M. ;
Starck, Jean-Luc .
IEEE TRANSACTIONS ON IMAGE PROCESSING, 2009, 18 (02) :310-321
[14]   Statistical image reconstruction for polyenergetic X-ray computed tomography [J].
Elbakri, IA ;
Fessler, JA .
IEEE TRANSACTIONS ON MEDICAL IMAGING, 2002, 21 (02) :89-99
[15]   ALGEBRAIC RECONSTRUCTION TECHNIQUES CAN BE MADE COMPUTATIONALLY EFFICIENT [J].
HERMAN, GT ;
MEYER, LB .
IEEE TRANSACTIONS ON MEDICAL IMAGING, 1993, 12 (03) :600-609
[16]   Quantifying Admissible Undersampling for Sparsity-Exploiting Iterative Image Reconstruction in X-Ray CT [J].
Jorgensen, Jakob S. ;
Sidky, Emil Y. ;
Pan, Xiaochuan .
IEEE TRANSACTIONS ON MEDICAL IMAGING, 2013, 32 (02) :460-473
[17]  
Kak A.C. Slaney M., 1999, PRINCIPLES COMPUTERI
[18]   Statistical inversion for medical x-ray tomography with few radiographs:: II.: Application to dental radiology [J].
Kolehmainen, V ;
Siltanen, S ;
Järvenpää, S ;
Kaipio, JP ;
Koistinen, P ;
Lassas, M ;
Pirttilä, J ;
Somersalo, E .
PHYSICS IN MEDICINE AND BIOLOGY, 2003, 48 (10) :1465-1490
[19]   Wavelet-based reconstruction for limited-angle X-ray tomography [J].
Rantala, M ;
Vänskä, S ;
Järvenpää, S ;
Kalke, M ;
Lassas, M ;
Moberg, J ;
Siltanen, S .
IEEE TRANSACTIONS ON MEDICAL IMAGING, 2006, 25 (02) :210-217
[20]   NONLINEAR TOTAL VARIATION BASED NOISE REMOVAL ALGORITHMS [J].
RUDIN, LI ;
OSHER, S ;
FATEMI, E .
PHYSICA D, 1992, 60 (1-4) :259-268