  Image super-resolution (SR) is a fast-moving field with novel architectures
attracting the spotlight. However, most SR models were optimized with dated
training strategies. In this work, we revisit the popular RCAN model and
examine the effect of different training options in SR. Surprisingly (or
perhaps as expected), we show that RCAN can outperform or match nearly all the
CNN-based SR architectures published after RCAN on standard benchmarks with a
proper training strategy and minimal architecture change. Besides, although
RCAN is a very large SR architecture with more than four hundred convolutional
layers, we draw a notable conclusion that underfitting is still the main
problem restricting the model capability instead of overfitting. We observe
supportive evidence that increasing training iterations clearly improves the
model performance while applying regularization techniques generally degrades
the predictions. We denote our simply revised RCAN as RCAN-it and recommend
practitioners to use it as baselines for future research. Code is publicly
available at https://github.com/zudi-lin/rcan-it.
