A qualitative investigation of optical flow algorithms for video denoising
This work addresses the need for better optical flow integration in video denoising for applications like media and automotive, but it is incremental as it focuses on qualitative evaluation without introducing new methods.
The paper investigates the qualitative performance of various optical flow algorithms, including classic and deep learning methods, when integrated into a state-of-the-art video denoising algorithm, using realistic content with challenging characteristics instead of standard images.
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.