  In recent years, methods based on deep learning have achieved unparalleled
performance at the cost of large computational complexity. In this work, we
propose an Efficient Multi-stage Video Denoising algorithm, called EMVD, to
drastically reduce the complexity while maintaining or even improving the
performance. First, a fusion stage reduces the noise through a recursive
combination of all past frames in the video. Then, a denoising stage removes
the noise in the fused frame. Finally, a refinement stage restores the missing
high frequency in the denoised frame. All stages operate on a transform-domain
representation obtained by learnable and invertible linear operators which
simultaneously increase accuracy and decrease complexity of the model. A single
loss on the final output is sufficient for successful convergence, hence making
EMVD easy to train. Experiments on real raw data demonstrate that EMVD
outperforms the state of the art when complexity is constrained, and even
remains competitive against methods whose complexities are several orders of
magnitude higher. The low complexity and memory requirements of EMVD enable
real-time video denoising on low-powered commercial SoC.
