  Convolutional Neural Networks have been the backbone of recent rapid progress
in Single-Image Super-Resolution. However, existing networks are very deep with
many network parameters, thus having a large memory footprint and being
challenging to train. We propose Large Receptive Field Networks which strive to
directly expand the receptive field of Super-Resolution networks without
increasing depth or parameter count. In particular, we use two different
methods to expand the network receptive field: 1-D separable kernels and atrous
convolutions. We conduct considerable experiments to study the performance of
various arrangement schemes of the 1-D separable kernels and atrous convolution
in terms of accuracy (PSNR / SSIM), parameter count, and speed, while focusing
on the more challenging high upscaling factors. Extensive benchmark evaluations
demonstrate the effectiveness of our approach.
