Improved optimization methods for image registration problems
Improved optimization methods for image registration problems
复制标题
DOI：
10.1007/s11075-018-0486-2
复制
发表时间：
2018-02
影响因子：
2.1
通讯作者：
Ke Chen;G. N. Grapiglia;Jinyun Yuan;Daoping Zhang
中科院分区：
文献类型：
--
作者：
Ke Chen;G. N. Grapiglia;Jinyun Yuan;Daoping Zhang
在本文中，我们提出了一种新的多级优化方法来最小化图像配准问题的离散化模型所得到的连续可微函数。这些多层格式依赖于一种新的两步高斯-牛顿方法，该方法在每次迭代中通过最小化目标函数在某个二维子空间上的二次逼近来计算第二步。在图像配准问题上的数值结果表明，该方法的性能优于标准的多层高斯-牛顿法。
In this paper, we propose new multilevel optimization methods for minimizing continuously differentiable functions obtained by discretizing models for image registration problems. These multilevel schemes rely on a novel two-step Gauss-Newton method, in which a second step is computed within each iteration by minimizing a quadratic approximation of the objective function over a certain two-dimensional subspace. Numerical results on image registration problems show that the proposed methods can outperform the standard multilevel Gauss-Newton method.