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ECT image reconstruction based on alternating direction approximate newton algorithm

机译:基于交替方向近似牛顿算法的ECT图像重建

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As a simple linearization of a highly nonlinear problem of image reconstruction in Electrical Capacitance Tomography (ECT), this approach suffers from drawbacks such as mismatches of position and size of the objects being imaged. To solve the ill-posed and nonlinear inverse problem of ECT image reconstruction, an alternating direction approximate Newton (ADAN) method is developed. The algorithm is based on the alternating direction method of multipliers (ADMM) and an approximation to Newton's method in which a term in Newton's Hessian is replaced by a Barzilai-Borwein (BB) approximation. The numerical experiments show that ADAN has the advantages of fast convergence speed and high imaging accuracy. Compared with the Landweber method, Newton's method and conjugate gradient algorithm, ADAN algorithm is a more stable process, image reconstruction quality is improved significantly. Especially for the cross model and sunrise model, compared with Newton iterative algorithm, image relative error reduced by 0.585 and 0.369, image correlation coefficient increased by 0.498 and 0.431.
机译:作为电容层析成像(ECT)中图像重建的高度非线性问题的简单线性化,此方法存在诸如被成像对象的位置和大小不匹配之类的缺点。为了解决ECT图像重建的不适定和非线性逆问题,提出了一种交替方向近似牛顿(ADAN)方法。该算法基于乘法器的交替方向方法(ADMM)和牛顿方法的近似值,在牛顿方法中,牛顿的Hessian项被Barzilai-Borwein(BB)近似值代替。数值实验表明,ADAN具有收敛速度快,成像精度高的优点。与Landweber方法,Newton方法和共轭梯度算法相比,ADAN算法处理过程更稳定,图像重建质量明显提高。特别是对于交叉模型和日出模型,与牛顿迭代算法相比,图像相对误差减小了0.585和0.369,图像相关系数增大了0.498和0.431。

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