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A good optical flow estimation is crucial in many video analysis andrestoration algorithms employed in application fields like media industry,industrial inspection and automotive. In this work, we investigate how welloptical flow algorithms perform qualitatively when integrated into a state ofthe 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) willbe taken into account. For the qualitative investigation, we will employrealistic content with challenging characteristic (noisy content, large motionetc.) instead of the standard images used in most publications.

A qualitative investigation of optical flow algorithms for video denoising