  We present a highly accurate single-image super-resolution (SR) method. Our
method uses a very deep convolutional network inspired by VGG-net used for
ImageNet classification \cite{simonyan2015very}. We find increasing our network
depth shows a significant improvement in accuracy. Our final model uses 20
weight layers. By cascading small filters many times in a deep network
structure, contextual information over large image regions is exploited in an
efficient way. With very deep networks, however, convergence speed becomes a
critical issue during training. We propose a simple yet effective training
procedure. We learn residuals only and use extremely high learning rates
($10^4$ times higher than SRCNN \cite{dong2015image}) enabled by adjustable
gradient clipping. Our proposed method performs better than existing methods in
accuracy and visual improvements in our results are easily noticeable.
