IEEE Trans Image Process. 2014 Oct;23(10):4527-38. doi: 10.1109/TIP.2014.2352497. Epub 2014 Aug 27.
Designing an appropriate regularizer is of great importance for accurate optical flow estimation. Recent works exploiting the nonlocal similarity and the sparsity of the motion field have led to promising flow estimation results. In this paper, we propose to unify these two powerful priors. To this end, we propose an effective flow regularization technique based on joint low-rank and sparse matrix recovery. By grouping similar flow patches into clusters, we effectively regularize the motion field by decomposing each set of similar flow patches into a low-rank component and a sparse component. For better enforcing the low-rank property, instead of using the convex nuclear norm, we use the log det(·) function as the surrogate of rank, which can also be efficiently minimized by iterative singular value thresholding. Experimental results on the Middlebury benchmark show that the performance of the proposed nonlocal sparse and low-rank regularization method is higher than (or comparable to) those of previous approaches that harness these same priors, and is competitive to current state-of-the-art methods.
设计合适的正则项对于准确的光流估计至关重要。最近的一些利用非局部相似性和运动场稀疏性的工作取得了很有前景的流估计结果。在本文中，我们提出将这两个强大的先验统一起来。为此，我们提出了一种基于联合低秩和稀疏矩阵恢复的有效流正则化技术。通过将相似的流补丁分组到聚类中，我们通过将每组相似的流补丁分解为一个低秩分量和一个稀疏分量来有效地正则化运动场。为了更好地强制低秩属性，我们使用对数行列式（log det(·)）函数而不是凸核范数作为秩的替代，通过迭代奇异值阈值化可以有效地最小化对数行列式（log det(·)）函数。在 Middlebury 基准上的实验结果表明，所提出的非局部稀疏和低秩正则化方法的性能高于（或与利用这些相同先验的先前方法相当），并且与当前最先进的方法具有竞争力。
