Image Super-Resolution Using Residual Global Context Network

Recent studies have showed that convolutional neural networks (CNN) can effectively improve the performance of single image super-resolution (SR). However, previous methods rarely considered long-range dependencies between pixels and channel-wise interdependencies at the same time. They ignores the fact that natural images have strong internal data repetition which requires the network to capture long-range dependencies between pixels and considering the interdepen-dencies between channels can better exploit the input information of the network. In addition, although past studies have proved that deep convolutional neural network benefit the performance of image super-resolution, it also means that the network needs more memory consumption and higher computational complexity. To solve these problem,we introduce Global Context block (GCB) and design a comparative shallow network called Residual Global Context Networks (RGC-N). It achieves a better trade-off between the amount of parameter and the quality of image reconstruction. Extensive experiments demonstrate that the proposed method is superior to the state-of-the-art methods.