  Deep convolutional neural networks perform better on images containing
spatially invariant noise (synthetic noise); however, their performance is
limited on real-noisy photographs and requires multiple stage network modeling.
To advance the practicability of denoising algorithms, this paper proposes a
novel single-stage blind real image denoising network (RIDNet) by employing a
modular architecture. We use a residual on the residual structure to ease the
flow of low-frequency information and apply feature attention to exploit the
channel dependencies. Furthermore, the evaluation in terms of quantitative
metrics and visual quality on three synthetic and four real noisy datasets
against 19 state-of-the-art algorithms demonstrate the superiority of our
RIDNet.
