  In this report we demonstrate that with same parameters and computational
budgets, models with wider features before ReLU activation have significantly
better performance for single image super-resolution (SISR). The resulted SR
residual network has a slim identity mapping pathway with wider (\(2\times\) to
\(4\times\)) channels before activation in each residual block. To further
widen activation (\(6\times\) to \(9\times\)) without computational overhead,
we introduce linear low-rank convolution into SR networks and achieve even
better accuracy-efficiency tradeoffs. In addition, compared with batch
normalization or no normalization, we find training with weight normalization
leads to better accuracy for deep super-resolution networks. Our proposed SR
network \textit{WDSR} achieves better results on large-scale DIV2K image
super-resolution benchmark in terms of PSNR with same or lower computational
complexity. Based on WDSR, our method also won 1st places in NTIRE 2018
Challenge on Single Image Super-Resolution in all three realistic tracks.
Experiments and ablation studies support the importance of wide activation for
image super-resolution. Code is released at:
https://github.com/JiahuiYu/wdsr_ntire2018
