论文标题
具有差异最佳运输的时空成像
Spatiotemporal Imaging with Diffeomorphic Optimal Transportation
论文作者
论文摘要
我们提出了一个具有差异最佳运输的变异模型,以进行关节图像重建和运动估计。所提出的模型是将瓦斯汀距离与Benamou组装在一起的生产 - 最佳运输中的Brenier公式以及与大变形差异度量映射有关的差异性的流动,这适用于具有大型的差异和质量分布的空间成像的情况。具体而言,我们首先使用Benamou-Brenier公式来表征质量保护图像流的最佳运输成本,并将速度场限制为可允许的Hilbert空间,以确保生成的变形流量是差异的。然后,我们获得了Benamou-Brenier公式的ODE限制的等效公式。最终,我们获得了遵循我们以前工作中提出的框架的ODE约束的建议模型。我们进一步获得等效的PDE受限最佳控制公式。理论上将提出的模型与几种现有替代方案进行了比较。提出了交替的最小化算法,用于求解具有ODE约束的拟议模型的时间消化版。还讨论了拟议模型和相关算法上的几个重要问题。特别是,我们根据提出的差异最佳运输提出了几种潜在模型。在适当的条件下,提出的算法还提供了一种新方案,以使用二次瓦斯汀距离来解决模型。最终通过时空断层扫描中的几个数值实验评估了性能,其中数据是从相关的顺序图像和/或各种噪声水平的相关顺序图像中测量的。
We propose a variational model with diffeomorphic optimal transportation for joint image reconstruction and motion estimation. The proposed model is a production of assembling the Wasserstein distance with the Benamou--Brenier formula in optimal transportation and the flow of diffeomorphisms involved in large deformation diffeomorphic metric mapping, which is suitable for the scenario of spatiotemporal imaging with large diffeomorphic and mass-preserving deformations. Specifically, we first use the Benamou--Brenier formula to characterize the optimal transport cost among the flow of mass-preserving images, and restrict the velocity field into the admissible Hilbert space to guarantee the generated deformation flow being diffeomorphic. We then gain the ODE-constrained equivalent formulation for Benamou--Brenier formula. We finally obtain the proposed model with ODE constraint following the framework that presented in our previous work. We further get the equivalent PDE-constrained optimal control formulation. The proposed model is compared against several existing alternatives theoretically. The alternating minimization algorithm is presented for solving the time-discretized version of the proposed model with ODE constraint. Several important issues on the proposed model and associated algorithms are also discussed. Particularly, we present several potential models based on the proposed diffeomorphic optimal transportation. Under appropriate conditions, the proposed algorithm also provides a new scheme to solve the models using quadratic Wasserstein distance. The performance is finally evaluated by several numerical experiments in space-time tomography, where the data is measured from the concerned sequential images with sparse views and/or various noise levels.