Fast, nonlocal and neural: a lightweight high quality solution to image
  denoising
Yu Guo Axel Davy Gabriele Facciolo Jean-Michel Morel Qiyu Jin
With the widespread application of convolutional neural networks (CNNs), the
traditional model based denoising algorithms are now outperformed. However,
CNNs face two problems. First, they are computationally demanding, which makes
their deployment especially difficult for mobile terminals. Second,
experimental evidence shows that CNNs often over-smooth regular textures
present in images, in contrast to traditional non-local models. In this letter,
we propose a solution to both issues by combining a nonlocal algorithm with a
lightweight residual CNN. This solution gives full latitude to the advantages
of both models. We apply this framework to two GPU implementations of classic
nonlocal algorithms (NLM and BM3D) and observe a substantial gain in both
cases, performing better than the state-of-the-art with low computational
requirements. Our solution is between 10 and 20 times faster than CNNs with
equivalent performance and attains higher PSNR. In addition the final method
shows a notable gain on images containing complex textures like the ones of the
MIT Moire dataset.