  In this paper, we propose a learning-based approach for denoising raw videos
captured under low lighting conditions. We propose to do this by first
explicitly aligning the neighboring frames to the current frame using a
convolutional neural network (CNN). We then fuse the registered frames using
another CNN to obtain the final denoised frame. To avoid directly aligning the
temporally distant frames, we perform the two processes of alignment and fusion
in multiple stages. Specifically, at each stage, we perform the denoising
process on three consecutive input frames to generate the intermediate denoised
frames which are then passed as the input to the next stage. By performing the
process in multiple stages, we can effectively utilize the information of
neighboring frames without directly aligning the temporally distant frames. We
train our multi-stage system using an adversarial loss with a conditional
discriminator. Specifically, we condition the discriminator on a soft gradient
mask to prevent introducing high-frequency artifacts in smooth regions. We show
that our system is able to produce temporally coherent videos with realistic
details. Furthermore, we demonstrate through extensive experiments that our
approach outperforms state-of-the-art image and video denoising methods both
numerically and visually.
