Improving Image Super-Resolution via Feature Re-Balancing Fusion

Recently, benefiting from the strong ability of feature representation, deep-learning-based methods have achieved excellent performance in single image super-resolution (SR). Furthermore, skip connection and feature fusion have been demonstrated to be a commendable strategy to deal with various features in different depth for informative reconstruction. Nevertheless, cross-layer features show diverse characteristic in detail representation, blindly fusion then introduces unavoidable interference of features. In this paper, we propose a novel feature fusion unit by utilizing alternative dilated convolutions for re-balancing diverse cross-layer features, named Feature Re-Balancing Fusion Network (RBFNet), which is theoretically and experimentally demonstrated to be robust to the interference in feature fusion for SR. Extensive experiments show that the proposed method achieves excellent performances quantitatively and qualitatively against the state-of-the-art methods.