  The non-local self-similarity property of natural images has been exploited
extensively for solving various image processing problems. When it comes to
video sequences, harnessing this force is even more beneficial due to the
temporal redundancy. In the context of image and video denoising, many
classically-oriented algorithms employ self-similarity, splitting the data into
overlapping patches, gathering groups of similar ones and processing these
together somehow. With the emergence of convolutional neural networks (CNN),
the patch-based framework has been abandoned. Most CNN denoisers operate on the
whole image, leveraging non-local relations only implicitly by using a large
receptive field. This work proposes a novel approach for leveraging
self-similarity in the context of video denoising, while still relying on a
regular convolutional architecture. We introduce a concept of patch-craft
frames - artificial frames that are similar to the real ones, built by tiling
matched patches. Our algorithm augments video sequences with patch-craft frames
and feeds them to a CNN. We demonstrate the substantial boost in denoising
performance obtained with the proposed approach.
