Accurate optical flow in noisy image sequences using flow adapted anisotropic diffusion

In this paper, we combine 3D anisotropic diffusion and motion estimation for image denoising and improvement of motion estimation. We compare different continuous isotropic nonlinear and anisotropic diffusion processes, which can be found in literature, with a process especially designed for image sequence denoising for motion estimation. All of these processes initially improve motion estimation due to reduction of noise and high frequencies. But while all the well known processes rapidly destroy or hallucinate motion information, the process brought forward here shows considerably less information loss or violation even at motion boundaries. We show the superior behavior of this process. Further we compare the performance of a standard finite difference diffusion scheme with several schemes using derivative filters optimized for rotation invariance. Using the discrete scheme with least smoothing artifacts we demonstrate the denoising capabilities of this approach. We exploit the motion estimation to derive an automatic stopping criterion.