Abstract
Accurate optical flow estimation with the frequency-domain regularization is a challenging problem in computer vision. In this paper, we solve this issue by introducing a novel optical flow method related to the frequency domain that uses TV-wavelet regularization. Specifically, we regard TV-wavelet regularization as a filtering process. After wavelet transform for optical flow field, we firstly remove outliers by performing a threshold operation. Then, we make up for lost motion information (such as flow edges and important motion details) determined by these missing or damaged wavelet coefficients by adding TV-wavelet coefficients that are obtained from transform spectrum of the prior flow geometrical features, which are controlled by the image structures. By combining the advantages of total variation to recover geometric structures with the strengths of wavelet representation to remove outliers, the proposed method significantly outperforms the current frequency-domain optical flow methods in removing outliers, preserving sharp flow edges, and restoring important motion details. It also shows competitive optical flow evaluation results on the challenging MPI-Sintel, Kitti, and Middlebury datasets.
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