Refined TV-L1 Optical Flow Estimation Using Joint Filtering

Though the accuracy and robustness of optical flow has been dramatically enhanced over the past few years, the issue of edge-blurring near the image and motion boundaries has remained a challenge in flow field estimation. In this paper, we propose a refined total variation with
L
¹
norm (TV-
L
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) optical flow estimation approach using joint filtering, named JOF. First, we divide the image into three categorized regions: mutual-structure regions, inconsistent structure regions, and smooth regions. The mutual-structure guided filter for optical flow estimation is constructed by extracting the mutual-structure regions of the flow field. Second, the refined TV-
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optical flow model is proposed by incorporating the non-local term and mutual-structure guided filter objective function into the classical TV-
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¹
energy function. Furthermore, the novel TV-
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optical flow objective function is minimized using a joint filtering program composed of a weighted median filter and a mutual-structure guided filter to optimize the estimated flow field during the coarse-to-fine optical flow computation scheme. Finally, we compare the proposed JOF method with several state-of-the-art approaches including variational and deep learning based optical flow models using the Middlebury, MPI-Sintel, and UCF101 test databases. The evaluation results indicate that the proposed method has high accuracy and good robustness for flow field computation and, especially, the significant benefit of edge-preserving.