It is known that optical flow estimation techniques suffer from the issues of ill-defined edges and boundaries of the moving objects. Traditional variational methods for optical flow estimation are not robust to handle these issues since the local filters in these methods do not hold the robustness near the edges. In this paper, we propose a non-local total variation NLTV-$L^1$ optical flow estimation method based on robust weighted guided filtering. Specifically, first, the robust weighted guided filtering objective function is proposed to preserve motion edges. The proposed objective function is based on the linear model which is computationally efficient and edge-preserving in complex natural scenarios. Second, the proposed weighted guided filtering objective function is incorporated into the non-local total variation NLTV-$L^1$ energy function. Finally, the novel NLTV-$L^1$ optical flow method is performed using the coarse-to-fine process. Additionally, we modify some state-of-the-art variational optical flow estimation methods by the robust weighted guided filtering objective function to verify the performance on Middlebury, MPI-Sintel, and Foggy Zurich sequences. Experimental results show that the proposed method can preserve edges and improve the accuracy of optical flow estimation compared with several state-of-the-art methods.

众所周知，光流估计技术存在着运动对象的边缘和边界不明确的问题。传统的用于光流估计的变分方法对于处理这些问题并不稳健，因为这些方法中的局部滤波器在边缘附近不具有稳健性。在本文中，我们提出了一种非局部总变化量NLTV-${\mathrm{大号}}^{\mathrm{1个}}$鲁棒加权导引滤波的光流估计方法 具体而言，首先，提出了鲁棒的加权引导滤波目标函数以保留运动边缘。所提出的目标函数基于线性模型，该线性模型在复杂的自然场景中具有高效的计算能力和边缘保留能力。其次，将拟议的加权导引滤波目标函数合并到非局部总变化量NLTV-${\mathrm{大号}}^{\mathrm{1个}}$能量函数。最后，小说NLTV-${\mathrm{大号}}^{\mathrm{1个}}$光学流动法是使用从粗到精的工艺进行的。此外，我们通过鲁棒的加权导引滤波目标函数修改了一些最新的变分光流估计方法，以验证在Middlebury，MPI-Sintel和Foggy Zurich序列上的性能。实验结果表明，与几种最新方法相比，该方法可以保留边缘并提高光流估计的准确性。