A non-local propagation filtering scheme for edge-preserving in variational optical flow computation

The median filtering heuristic is considered to be an indispensable tool for the currently popular variational optical flow computation. Its attractive advantages are that outliers reduction is attained while image edges and motion boundaries are preserved. However, it still may generate blurring at image edges and motion boundaries caused by large displacement, motion occlusion, complex texture, and illumination change. In this paper, we present a non-local propagation filtering scheme to deal with the above problem during the coarse-to-fine optical flow computation. First, we analyze the connection between the weighted median filtering and the blurring of image edge and motion boundary under the coarse-to-fine optical flow computing scheme. Second, to improve the quality of the initial flow field, we introduce a non-local propagation filter to reduce outliers while preserving context information of the flow field. Furthermore, we present an optimization combination of non-local propagation filtering and weighted median filtering for the flow field estimation under the coarse-to-fine scheme. Extensive experiments on public optical flow benchmarks demonstrate that the proposed scheme can effectively improve the accuracy and robustness of optical flow estimation.