  We present a method for converting denoising neural networks from spatial
into spatio-temporal ones by modifying the network architecture and loss
function. We insert Robust Average blocks at arbitrary depths in the network
graph. Each block performs latent space interpolation with trainable weights
and works on the sequence of image representations from the preceding spatial
components of the network. The temporal connections are kept live during
training by forcing the network to predict a denoised frame from subsets of the
input sequence. Using temporal coherence for denoising improves image quality
and reduces temporal flickering independent of scene or image complexity.
