Lightweight global-locally connected distillation network for single image super-resolution

As convolutional neural networks (CNNs) have been commonly applied to ill-posed single image super-resolution (SISR) task, most previous CNN-based methods made significant progress in terms of both high signal-to-noise ratios (PSNR) and structural similarity (SSIM). However, with the layers in those networks going deeper and deeper, they require more and more computing power, fail to consider distilling the feature maps. In this paper, we propose a lightweight global-locally connected distillation network, GLCDNet. Specifically, we propose a wide activation shrink-expand convolutional block whose filter channels will first shrink then expand to aggregate more information. This information will concatenate with feature maps of the previous blocks to further explore shallow information. Thus, the block will exploit statistics within most feature channels while refining useful information of features. Furthermore, together with the global-local connection method, our network is robust to benchmark datasets with high processing speed. Comparative results demonstrate that our GLCDNet achieves superior performance while keeping the parameters and speed balanced.