Abstract. Recent studies have shown that deep neural networks can significantly improve the quality of single-image super-resolution. Current
researches tend to use deeper convolutional neural networks to enhance
performance. However, blindly increasing the depth of the network cannot ameliorate the network effectively. Worse still, with the depth of the
network increases, more problems occurred in the training process and
more training tricks are needed. In this paper, we propose a novel multiscale residual network (MSRN) to fully exploit the image features, which
outperform most of the state-of-the-art methods. Based on the residual
block, we introduce convolution kernels of different sizes to adaptively
detect the image features in different scales. Meanwhile, we let these
features interact with each other to get the most efficacious image information, we call this structure Multi-scale Residual Block (MSRB).
Furthermore, the outputs of each MSRB are used as the hierarchical features for global feature fusion. Finally, all these features are sent to the
reconstruction module for recovering the high-quality image