Multilevel strategies are an integral part of many image registration algorithms. These strategies are very well-known for avoiding undesirable local minima, providing an outstanding initial guess, and reducing overall computation time. State-of-the-art multilevel strategies build a hierarchy of discretization in the spatial dimensions. In this paper, we present a spatio-temporal strategy, where we introduce a hierarchical discretization in the temporal dimension at each spatial level. This strategy is suitable for a motion estimation problem where the motion is assumed smooth over time. Our strategy exploits the temporal smoothness among image frames by following a predictor-corrector approach. The strategy predicts the motion by a novel interpolation method and later corrects it by registration. The prediction step provides a good initial guess for the correction step, hence reduces the overall computational time for registration. The acceleration is achieved by a factor of 2.5 on average, over the state-of-the-art multilevel methods on three examined optical coherence tomography datasets.

中文翻译：

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通过时空多级策略加速图像序列的配准

多级策略是许多图像配准算法的组成部分。这些策略因避免不希望的局部最小值、提供出色的初始猜测和减少总体计算时间而闻名。最先进的多级策略在空间维度上建立了离散化的层次结构。在本文中，我们提出了一种时空策略，我们在每个空间级别的时间维度中引入了分层离散化。该策略适用于假设运动随时间平滑的运动估计问题。我们的策略通过遵循预测校正方法来利用图像帧之间的时间平滑性。该策略通过一种新颖的插值方法预测运动，然后通过配准对其进行校正。预测步骤为校正步骤提供了良好的初始猜测，因此减少了配准的整体计算时间。在三个检查的光学相干断层扫描数据集上，与最先进的多级方法相比，加速平均达到 2.5 倍。