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

页码：236 / 245

页数：10

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