  Bursts of images exhibit significant self-similarity across both time and
space. This motivates a representation of the kernels as linear combinations of
a small set of basis elements. To this end, we introduce a novel basis
prediction network that, given an input burst, predicts a set of global basis
kernels -- shared within the image -- and the corresponding mixing coefficients
-- which are specific to individual pixels. Compared to state-of-the-art
techniques that output a large tensor of per-pixel spatiotemporal kernels, our
formulation substantially reduces the dimensionality of the network output.
This allows us to effectively exploit comparatively larger denoising kernels,
achieving both significant quality improvements (over 1dB PSNR) and faster
run-times over state-of-the-art methods.
