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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 receptivedoi:10.1109/cvprw.2018.00120 dblp:conf/cvpr/SeifA18 fatcat:zjitzzbkp5cexldguptskfjs7y