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
[21]   A constrained, total-variation minimization algorithm for low-intensity x-ray CT [J].
Sidky, Emil Y. ;
Duchin, Yuval ;
Pan, Xiaochuan ;
Ullberg, Christer .
MEDICAL PHYSICS, 2011, 38 :S117-S125
[22]   Theory and algorithms for image reconstruction on chords and within regions of interest [J].
Zou, Y ;
Pan, XC ;
Sidky, EY .
JOURNAL OF THE OPTICAL SOCIETY OF AMERICA A-OPTICS IMAGE SCIENCE AND VISION, 2005, 22 (11) :2372-2384