TVR-DART: A More Robust Algorithm for Discrete Tomography From Limited Projection Data With Automated Gray Value Estimation
TVR-DART: A More Robust Algorithm for Discrete Tomography From Limited Projection Data With Automated Gray Value Estimation
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DOI：
10.1109/tip.2015.2504869
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发表时间：
2016
影响因子：
10.6
通讯作者：
X. Zhuge;W. J. Palenstijn;K. Batenburg
中科院分区：
文献类型：
--
作者：
X. Zhuge;W. J. Palenstijn;K. Batenburg
本文提出了一种新的离散层析成像（DT）迭代重建算法——基于灰度值自动估计的全变分正则化离散代数重建技术（TVR-DART）。该算法比原来的DART算法具有更强的鲁棒性和自动化，并且针对仅由几种不同材料组成的物体进行成像，每种材料在重建中对应不同的灰度值。TVR-DART通过同时利用扫描对象的两种先验知识，在压缩感知启发的优化框架内解决离散重建问题，将当前重建导向具有指定数量的离散灰度值的解。随着迭代重建的改进，对灰度值和阈值进行估计。从模拟数据、实验μCT和电子断层扫描数据集进行的大量实验表明，在噪声条件下，TVR-DART能够从少量投影图像和/或小角度范围内提供比现有算法更准确的重建。此外，与原DART算法相比，新算法所需的参数调优量更小。通过TVR-DART，我们旨在为断层摄影界提供一种易于使用且鲁棒的DT算法。
In this paper, we present a novel iterative reconstruction algorithm for discrete tomography (DT) named total variation regularized discrete algebraic reconstruction technique (TVR-DART) with automated gray value estimation. This algorithm is more robust and automated than the original DART algorithm, and is aimed at imaging of objects consisting of only a few different material compositions, each corresponding to a different gray value in the reconstruction. By exploiting two types of prior knowledge of the scanned object simultaneously, TVR-DART solves the discrete reconstruction problem within an optimization framework inspired by compressive sensing to steer the current reconstruction toward a solution with the specified number of discrete gray values. The gray values and the thresholds are estimated as the reconstruction improves through iterations. Extensive experiments from simulated data, experimental μCT, and electron tomography data sets show that TVR-DART is capable of providing more accurate reconstruction than existing algorithms under noisy conditions from a small number of projection images and/or from a small angular range. Furthermore, the new algorithm requires less effort on parameter tuning compared with the original DART algorithm. With TVR-DART, we aim to provide the tomography society with an easy-to-use and robust algorithm for DT.