  With the advent of mobile phone photography and point-and-shoot cameras,
deep-burst imaging is widely used for a number of photographic effects such as
depth of field, super-resolution, motion deblurring, and image denoising. In
this work, we propose to solve the problem of deep-burst image denoising by
including an optical flow-based correspondence estimation module which aligns
all the input burst images with respect to a reference frame. In order to deal
with varying noise levels the individual burst images are pre-filtered with
different settings. Exploiting the established correspondences one network
block predicts a pixel-wise spatially-varying filter kernel to smooth each
image in the original and prefiltered bursts before fusing all images to
generate the final denoised output. The resulting pipeline achieves
state-of-the-art results by combining all available information provided by the
burst.
