Deep Hierarchical Single Image Super-Resolution by Exploiting Controlled Diverse Context Features

This paper presents a hierarchical convolutional neural network (CNN) for single image super-resolution (SISR), which exploits the controlled multi-context features. We focus on the method to extract more enriched features than the case of using fixed size kernels. For this, we attempt to bring out the best of the given parameter capacity through the design of some sophisticated networks in a hierarchical manner. First, we exploit the multi-kernel dilated convolution for extracting multi-size contexts from the image and combine them with the proposed trainable parameters. The multi-kernel network with some new pre-and post-processing blocks forms our basic building block. Then the basic building blocks are densely connected with a new feature fusion schemes, which makes the upper level building block. Then, by connecting the upper level blocks, we can use various features which can enrich the representation of the images. In the experiments, it is shown that the proposed method achieves significant PSNR gain compared to recent lightweight models with comparable numbers of parameters.