FC$^2$N: Fully Channel-Concatenated Network for Single Image Super-Resolution

Most current image super-resolution (SR) methods based on deep convolutional neural networks (CNNs) use residual learning in network structural design, which contributes to effective back propagation, thus improving SR performance by increasing model scale. However, deep residual network suffers some redundancy in model representational capacity by introducing short paths, thus hindering the full mining of model capacity. In addition, blindly enlarging the model scale will cause more problems in model training, even with residual learning. In this work, a novel network architecture is introduced to fully exploit the representational capacity of the model, where all skip connections are implemented by weighted channel concatenation, followed by a 1$\times$1 conv layer. Based on this weighted skip connection, we construct the building modules of our model, and improve the global feature fusion (GFF). Unlike most previous models, all skip connections in our network are channel-concatenated and no residual connection is adopted. It is therefore termed as fully channel-concatenated network (FC$^2$N). Due to the full exploitation of model capacity, the proposed FC$^2$N achieves better performance than other advanced models with fewer model parameters. Extensive experiments demonstrate the superiority of our method to other methods, in terms of both quantitative metrics and visual quality.