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:
this https URL

本文研究了超分辨率的残差网络中宽激活对性能的重要性，提出了 WDSR 模型，并证明其在大规模图像超分辨率基准测试中具有更好的准确性和效率，同时也在 2018 年 NTIRE 单图像超分辨率挑战中取得了好成绩。