Fast and accurate optical flow estimation is a challenging problem in computer vision. In this paper, we present a novel model to solve the optical flow problem by combining the strengths of the Split Bregman method with the advantages of an efficient variational framework. It allows us to employ different regularization tensors for the Split Bregman regularizer to preserve motion discontinuities. Simultaneously, a novel nonlocal Split Bregman method with adaptive support weights is also developed for the smoothness regularization to preserve motion details, repel outliers, enhance contrast, and reduce motion blurring and staircase effect. The proposed algorithm shows significant improvements in preserving sharp flow edges and important motion details. Most of all, it requires only a few iterations to achieve fast convergence and runs faster than the classic TV-L1 flow method. Our method significantly outperforms the current state-of-the-art methods on the challenging MPI-Sintel dataset and shows good performance on the Kitti flow 2015 and Middlebury datasets.

We present a novel optical flow estimation framework that reinterprets most popular algorithms (e.g., the Horn- Schunck model, PDE-based models, TV-based models, and nonlocal-based models) from an iterative filtering perspective. Different regularizers in the related optical flow estimation algorithms can be achieved by adopting different filtering operations. The key merit of the proposed framework is that a variety of filtering models and corresponding optimization strategies, which are used in image processing, can be directly utilized in the regularization term, leading to a convenient way of designing appropriate optical flow estimation algorithms. Under the proposed filtering framework, we demonstrate easily designing optical flow estimation algorithms with respect to translation consistency, rotation consistency, and divergence consistency constraints by adopting different spatial filters, respectively. We also derive a novel optical flow estimation algorithm with 3D filtering based model for the regularization term, which makes use of nonlocal self-similarity and sparse character of optical flow field. Benefit from the advantages of patch-based nonlocal sparse regularizer, the derived algorithm can remove outliers while preserving sharp flow edges and important motion details. At present, the proposed algorithm achieves state-of-the-art results on the challenging MPI-Sintel dataset and shows good performance on the Kitti flow 2015 and Middlebury datasets.

Efficient optical flow estimation with high accuracy is a challenging problem in computer vision. In this paper, we present a simple but efficient segmentation-based PatchMatch framework to address this issue. Specifically, it firstly generates sparse seeds without losing important motion information by oversegmentation, and then yields sparse matches by adopting a coarse-to-fine PatchMatch with sparse seeds. Such a scheme enhances the robustness of global regularization and yields better matching results compared with the existing NNF techniques while leading to a significant speed-up due to the sparsity of these seeds. Simultaneously, we introduce an extended nonlocal propagation and adaptive random search to address the basic limitation of traditional coarse-to-fine framework in handing motion details that often vanish at coarser levels. Finally, we obtain dense matches at the finest level through an efficient sparse-todense matching according to the cues of oversegmentation. While performing an efficient approximation for oversegmentation, the proposed algorithm runs significantly fast and are robust to large displacements while preserving important motion details. It also achieves good performance on the challenging MPI-Sintel and Kitti flow 2015 datasets.

In this paper, we proposed a novel motion model with Helmholtz decomposition for complex fluid flows in a filtering-based optical flow framework, where the optimization of the regularization term is treated as a filtering process, and different motion patterns can be captured by finding appropriate filter kernels based on the designed filters. In this framework, we introduce a novel optical flow method with a joint spatial filter, which is based on the Helmholtz decomposition theorem that assumes a local motion field is composed of a curl field and a divergence field. By adjusting the scale of the weights in the filter kernels and combining a curl filter with a divergence filter in a certain ratio, it can simulate different motion patterns. In addition, if the correlation between the horizontal and vertical components of the optical flow field in the filter kernel is eliminated, it will be transformed into a linear motion model. Based on this linear motion model, we also develop a novel optical flow method with an adaptive guided filter. By finding an adaptive filter kernel driven by both the input image and the guided motion field for the designed filter, it can successfully capture different motion patterns, and yield an edge-preserving smoothing optical flow field. Most importantly, the proposed optical flow model provides a new way to design the regularizers for capturing different motion patterns in complex fluid flow. In particular, the designed optical flow method with an adaptive guided filter significantly outperforms the current state-of-the-art optical flow methods in predicting complex fluid flows.