G-HAPNet: A Novel Structure for Single Image Super-Resolution

Recent investigations on single image super-resolution (SISR) have progressed with the development of deep convolutional neural networks (DCNNs). However, increasingly complex network designs cause huge computational budgets. Therefore, a more efficient structure for SISR task is desirable. In this report, we propose a novel structure, called G-HAPNet. Specifically, the group-hierarchical atrous pyramid block (G-HAPB) is built to package as a general block for deeper network constitution. Firstly, the original features are expanded and grouped in channel. Then, a atrous pyramid is constructed to extract multi-scale features from corresponding channels. Besides, we introduce hierarchical grouping aggregation (HGA) which includes forward aggregation and backward aggregation by skip connections so that we can achieve hierarchical fusion and information guidance among multi-scale features. Extensive experiments demonstrate that with the same level depth and computational budgets, our proposed G-HAPNet has better performance than state-of-the-art methods on both synthetic datasets and real-world dataset, which indicates our G-HAPNet is a more efficient and practical structure for SISR.