  Current video denoising methods perform temporal fusion by designing
convolutional neural networks (CNN) or combine spatial denoising with temporal
fusion into basic recurrent neural networks (RNNs). However, there have not yet
been works which adapt gated recurrent unit (GRU) mechanisms for video
denoising. In this letter, we propose a new video denoising model based on GRU,
namely GRU-VD. First, the reset gate is employed to mark the content related to
the current frame in the previous frame output. Then the hidden activation
works as an initial spatial-temporal denoising with the help from the marked
relevant content. Finally, the update gate recursively fuses the initial
denoised result with previous frame output to further increase accuracy. To
handle various light conditions adaptively, the noise standard deviation of the
current frame is also fed to these three modules. A weighted loss is adopted to
regulate initial denoising and final fusion at the same time. The experimental
results show that the GRU-VD network not only can achieve better quality than
state of the arts objectively and subjectively, but also can obtain satisfied
subjective quality on real video.
