TV-L1 Optical Flow Estimation with Image Details Recovering Based on Modified Census Transform

This paper proposes an improved optical flow estimation approach based on the total variational L1 minimization technique with weighted median filter. Furthermore, recovering image details using modified census transform algorithm improves the overall accuracy of estimating large scale displacements optical flow. On the other hand, the use of the Taylor expansion approximation in most of the optical flow approaches limits the ability to estimate movement of fast objects. Hence, a coarse-to-fine scheme is used to overcome such a problem of the cost of losing small details in the interpolation process where initial values are propagated from the coarse level to the fine one. The proposed algorithm improves the accuracy of the estimation process by integrating the correspondence results of the modified census transform into the coarse-to-fine module in order to recover the lost details. The outcome of the proposed approach yields state-of-the-art results on the Middlebury optical flow evaluations.