Accurate optical flow estimation in noisy sequences by robust tensor-driven anisotropic diffusion

In this paper, a new tensor-driven anisotropic diffusion filtering method is proposed for achieving accurate optical flow estimation in noisy image sequences. The novelties of our approach are: (1) robust tensor-driven anisotropic diffusion computation, (2) new thresholding criterion for normalization function. By utilizing the decomposed eigenvectors and eigenvalues of the 3D structure tensor, the robust diffusion tensor is computed to steer the anisotropic filtering over the input image sequence. The moving orientations of the local spatio-temporal structures are precisely captured during the denoising process. For achieving more accurate diffusion tensor computation, a new thresholding criterion is developed in the normalization function to threshold the decomposed eigenvalues. As compared with that of existing methods, our experimental results demonstrate much improved accuracy on both motion field classification and optical flow estimation.