Image Super-Resolution With Content-Aware Feature Processing

Abstract

Image super-resolution (SR) is currently a very active research topic with applications spanning from computer vision to videos and graphic industries. The top performers in SR field usually employ deep or wide convolutional neural networks (CNNs) to restore the lost textures from low-resolution images. However, most of these methods adopt pixel-shuffle, and deconvolution as their up-sampling techniques and often generate conspicuous artifacts in the reconstructed image. Additionally, the ongoing trend of directly portraying the degraded low-resolution image to high-resolution via complex deep CNNs improves the reconstruction performance, but at the cost of high computational complexity. In this work, we propose a multi-level bi-cubic up-sampler network (MBUp-Net) for reconstructing high-quality SR image with restricted number of parameters. A novel Content-Aware Feature Difference (CAFD) block is presented to reform the network by focusing on contextual information. The proposed CAFD block consists of four multi-level attention blocks for better extraction of low-level features at different scales. Furthermore, we design an innovative up-sampling layer that consistently outperforms the traditional up-sampling methods. These components collaboratively endow our proposed network with a great performance boost, helping it achieve state-of-the-art accuracy on five synthetic benchmark datasets, both qualitatively and quantitatively. In addition, a detailed ablation study has been accomplished to scrutinize the improvements obtained by different modules in the proposed method.