Optical flow with fractional order regularization: variational model and solution method
This work offers an incremental improvement to optical flow estimation by introducing fractional order regularization, but lacks broad impact or SOTA results.
The authors propose a variational optical flow model using fractional order regularization with L1 norm, solved via split Bregman. Experiments show favorable performance compared to an existing method, with optimal fractional order depending on image geometry and texture.
An optical flow variational model is proposed for a sequence of images defined on a domain in $\mathbb{R}^2$. We introduce a regularization term given by the $L^1$ norm of a fractional differential operator. To solve the minimization problem we apply the split Bregman method. Extensive experimental results, with performance evaluation, are presented to demonstrate the effectiveness of the new model and method and to show that our algorithm performs favorably in comparison to another existing method. We also discuss the influence of the order $α$ of the fractional operator in the estimation of the optical flow, for $0 \leq α\leq 2$. We observe that the values of $α$ for which the method performs better depends on the geometry and texture complexity of the image. Some extensions of our algorithm are also discussed.