Image Super-resolution via Deep Aggregation Network

Deep convolutional neural networks (CNNs) have recently made a considerable achievement in the single-image super-resolution (SISR) problem. Most CNN architectures for SIS-R incorporate skip connections to integrate features, and treat them equally. However, this neglects the discrimination of features, and consequently, achieving relatively poor performance. To address this problem, we introduce a deep aggregation network that merging extraction and aggregation nodes in a tree structure, which can aggregate features progressively. In particular, we rescale the information in the aggregation node by modelling the interaction between channels, which shares the same insight on the attention mechanism for improving the discriminative ability of network. In the extraction node, we introduce an mlpconv layer into a dense unit that is parallel to the convolutional layer and can improve the nonlinear mapping capability, where the residual learning is utilized to accelerate the training process. Extensive experiments conducted on several publicly available datasets have demonstrated the superiority of our model over state-of-the-art in objective metrics and visual impressions.