MANet: Improving Video Denoising with a Multi-Alignment Network

02/20/2022
by   Yaping Zhao, et al.
0

In video denoising, the adjacent frames often provide very useful information, but accurate alignment is needed before such information can be harnassed. In this work, we present a multi-alignment network, which generates multiple flow proposals followed by attention-based averaging. It serves to mimics the non-local mechanism, suppressing noise by averaging multiple observations. Our approach can be applied to various state-of-the-art models that are based on flow estimation. Experiments on a large-scale video dataset demonstrate that our method improves the denoising baseline model by 0.2dB, and further reduces the parameters by 47

READ FULL TEXT

Please sign up or login with your details

Forgot password? Click here to reset