High-order total variation regularization approach for axially symmetric object tomography from a single radiograph
Publication in refereed journal

香港中文大學研究人員
替代計量分析
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其它資訊
摘要In this paper, we consider tomographic reconstruction for axially symmetric objects from a single radiograph formed by fan-beam X-rays. All contemporary methods are based on the assumption that the density is piecewise constant or linear. From a practical viewpoint, this is quite a restrictive approximation. The method we propose is based on high-order total variation regularization. Its main advantage is to reduce the staircase effect while keeping sharp edges and enable the recovery of smoothly varying regions. The optimization problem is solved using the augmented Lagrangian method which has been recently applied in image processing. Furthermore, we use a one-dimensional (1D) technique for fan-beam X-rays to approximate 2D tomographic reconstruction for cone-beam X-rays. For the 2D problem, we treat the cone beam as fan beam located at parallel planes perpendicular to the symmetric axis. Then the density of the whole object is recovered layer by layer. Numerical results in 1D show that the proposed method has improved the preservation of edge location and the accuracy of the density level when compared with several other contemporary methods. The 2D numerical tests show that cylindrical symmetric objects can be recovered rather accurately by our high-order regularization model.
著者Chan R.H., Liang H., Wei S., Nikolova M., Tai X.-C.
期刊名稱Inverse Problems and Imaging
出版年份2015
月份1
日期1
卷號9
期次1
出版社American Institute of Mathematical Sciences
出版地United States
頁次55 - 77
國際標準期刊號1930-8337
電子國際標準期刊號1930-8345
語言英式英語
關鍵詞Abel inversion, Augmented Lagrangian method, High-order total variation, Radiograph, Tomography

上次更新時間 2020-25-10 於 00:33