Open AccessComputed tomography for high-speed rotation object
Author(s) -
Defeng Chen,
Hongwei Li,
Qian Wang,
Peng Zhang,
Yining Zhu
Publication year - 2015
Publication title -
optics express
Language(s) - Uncategorized
Resource type - Journals
SCImago Journal Rank - 1.394
H-Index - 271
ISSN - 1094-4087
DOI - 10.1364/oe.23.013423
Subject(s) - computer science , rotation (mathematics) , deconvolution , iterative reconstruction , computer vision , artificial intelligence , tomography , object (grammar) , algorithm , optics , physics
Computed tomography (CT) for inspecting high-speed rotation objects (HRO) is difficult. Images reconstructed directly by conventional reconstruction algorithms would usually suffer from motion blurring. Currently, studies on this topic are very few. In this paper, we build a mathematical model to describe the scanning data for HRO and establish a principle for choosing the sampling time, which results in a deconvolution model. The idea of split Bregman is utilized to solve the model efficiently. Then, from the deconvoluted data, the image of HRO is reconstructed by conventional reconstruction algorithms. Experiments on simulation data as well as real data are provided to verify the effectiveness of our approach.