  Tremendous advances in image restoration tasks such as denoising and
super-resolution have been achieved using neural networks. Such approaches
generally employ very deep architectures, large number of parameters, large
receptive fields and high nonlinear modeling capacity. In order to obtain
efficient and fast image restoration networks one should improve upon the above
mentioned requirements.
  In this paper we propose a novel activation function, the multi-bin trainable
linear unit (MTLU), for increasing the nonlinear modeling capacity together
with lighter and shallower networks. We validate the proposed fast image
restoration networks for image denoising (FDnet) and super-resolution (FSRnet)
on standard benchmarks. We achieve large improvements in both memory and
runtime over current state-of-the-art for comparable or better PSNR accuracies.
