  Many classic methods have shown non-local self-similarity in natural images
to be an effective prior for image restoration. However, it remains unclear and
challenging to make use of this intrinsic property via deep networks. In this
paper, we propose a non-local recurrent network (NLRN) as the first attempt to
incorporate non-local operations into a recurrent neural network (RNN) for
image restoration. The main contributions of this work are: (1) Unlike existing
methods that measure self-similarity in an isolated manner, the proposed
non-local module can be flexibly integrated into existing deep networks for
end-to-end training to capture deep feature correlation between each location
and its neighborhood. (2) We fully employ the RNN structure for its parameter
efficiency and allow deep feature correlation to be propagated along adjacent
recurrent states. This new design boosts robustness against inaccurate
correlation estimation due to severely degraded images. (3) We show that it is
essential to maintain a confined neighborhood for computing deep feature
correlation given degraded images. This is in contrast to existing practice
that deploys the whole image. Extensive experiments on both image denoising and
super-resolution tasks are conducted. Thanks to the recurrent non-local
operations and correlation propagation, the proposed NLRN achieves superior
results to state-of-the-art methods with much fewer parameters.
