Two-Stage Convolutional Network for Image Super-Resolution

Deep convolutional neural networks (DCNN) have recently advanced the state-of-the-art on the issue of single image super-resolution (SR). In this work, we propose a two-stage convolutional network (TSCN) to estimate the desired high-resolution (HR) image from the corresponding low-resolution (LR) image. Specifically, we propose the multi-path information fusion (MIF) module that collects abundant information from feature maps of the input, output and intermediary in a module and distills primary information therein. Several cascaded MIF modules are used to progressively extract features desired by reconstruction and the output of each module is gathered for rebuilding the HR image. In addition, we introduce a refinement network with local residual topology architecture as the second stage so as to further restore the high-frequency details of HR image produced by the first stage. Due to less number of filters, the compact model achieves fast inference time and brings about state-of-the-art SR results on four benchmark datasets simultaneously. Code is available at https://github.com/Zheng222/TSCN.