  A good optical flow estimation is crucial in many video analysis and
restoration algorithms employed in application fields like media industry,
industrial inspection and automotive. In this work, we investigate how well
optical flow algorithms perform qualitatively when integrated into a state of
the art video denoising algorithm. Both classic optical flow algorithms (e.g.
TV-L1) as well as recent deep learning based algorithm (like RAFT or BMBC) will
be taken into account. For the qualitative investigation, we will employ
realistic content with challenging characteristic (noisy content, large motion
etc.) instead of the standard images used in most publications.
