CT理论与应用研究2017，Vol.26Issue(4)：435-445,11.DOI:10.15953/j.1004-4140.2017.26.04.05
基于Abel变换的图像重建自适应方法
Abel Transformation Based Adaptive Regularization Approach for Image Reconstruction
摘要
Abstract
In this paper, we discuss an adaptive regularization approach for density reconstruction of axially symmetric object whose tomography comes from a single X-ray projection. The method we proposed is based on the combination of total variation regularization and high-order total variation regularization. Its main advantage is to reduce the staircase effect while keeping sharp edges and recovering smoothly varying regions. Moreover, it simplifies the use of parameters. We apply the augmented Lagrangian method to solve the optimization involved. Numerical results show that the proposed method has improved the accuracy of density edges and values. Besides, the method is not sensitive to the measured data noise.关键词
层析成像/自适应/高阶全变分正则化模型/增广拉格朗日方法/Abel逆变换Key words
CT/adaptive/high-order total variation regularization/augmented Lagrangian method/Abel inversion分类
数理科学引用本文复制引用
杜健鹏,梁海霞,魏素花..基于Abel变换的图像重建自适应方法[J].CT理论与应用研究,2017,26(4):435-445,11.基金项目
国家自然科学基金(11571003) （11571003）
江苏省自然科学基金青年项目(BK20150373). （BK20150373）
