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Neural Compression-Based Feature Learning for Video Restoration. (arXiv:2203.09208v2 [cs.CV] UPDATED)
March 21, 2022, 1:11 a.m. | Cong Huang, Jiahao Li, Bin Li, Dong Liu, Yan Lu
cs.CV updates on arXiv.org arxiv.org
How to efficiently utilize the temporal features is crucial, yet challenging,
for video restoration. The temporal features usually contain various noisy and
uncorrelated information, and they may interfere with the restoration of the
current frame. This paper proposes learning noise-robust feature
representations to help video restoration. We are inspired by that the neural
codec is a natural denoiser. In neural codec, the noisy and uncorrelated
contents which are hard to predict but cost lots of bits are more inclined to …
More from arxiv.org / cs.CV updates on arXiv.org
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