  Recent years have witnessed great success of convolutional neural network
(CNN) for various problems both in low and high level visions. Especially
noteworthy is the residual network which was originally proposed to handle
high-level vision problems and enjoys several merits. This paper aims to extend
the merits of residual network, such as skip connection induced fast training,
for a typical low-level vision problem, i.e., single image super-resolution. In
general, the two main challenges of existing deep CNN for supper-resolution lie
in the gradient exploding/vanishing problem and large numbers of parameters or
computational cost as CNN goes deeper. Correspondingly, the skip connections or
identity mapping shortcuts are utilized to avoid gradient exploding/vanishing
problem. In addition, the skip connections have naturally centered the
activation which led to better performance. To tackle with the second problem,
a lightweight CNN architecture which has carefully designed width, depth and
skip connections was proposed. In particular, a strategy of gradually varying
the shape of network has been proposed for residual network. Different residual
architectures for image super-resolution have also been compared. Experimental
results have demonstrated that the proposed CNN model can not only achieve
state-of-the-art PSNR and SSIM results for single image super-resolution but
also produce visually pleasant results. This paper has extended the mmm 2017
oral conference paper with a considerable new analyses and more experiments
especially from the perspective of centering activations and ensemble behaviors
of residual network.
