A Novel Multiconnected Convolutional Network for Super-Resolution

Convolutional neural networks exhibit superior performance for single image super-resolution (SISR) tasks. However, as the network grows deeper, features from the earlier layers are impeded or less used in later layers. In SISR, the earlier layers are mainly composed of local features that are essential to the task. In this letter, we present a novel multiconnected convolutional network for SISR tasks by enhancing the combination of both low- and high-level features. We design a structure built on multiconnected blocks to extract diversified and complicated features via the concatenation of low-level features to high-level features. In addition to stacking multiconnected blocks, a long skip-connection is implemented to further aggregate features of the first layer and a specific later layer. Furthermore, we employ a flexible two-parameter loss function to optimize the training process. The proposed method yields state-of-the-art performance both in terms of quantitative metrics and visual quality. The method also outperforms existing methods on datasets via unknown degrading operators, indicating an excellent generalization ability.