Single image super-resolution based on directional variance attention network

Recent advances in single image super-resolution (SISR) explore the power of deep convolutional neu-ral networks (CNNs) to achieve better performance. However, most of the progress has been made by scaling CNN architectures, which usually raise computational demands and memory consumption. This makes modern architectures less applicable in practice. In addition, most CNN-based SR methods do not fully utilize the informative hierarchical features that are helpful for final image recovery. In order to address these issues, we propose a directional variance attention network (DiVANet), a computationally efficient yet accurate network for SISR. Specifically, we introduce a novel directional variance attention (DiVA) mechanism to capture long-range spatial dependencies and exploit inter-channel dependencies si-multaneously for more discriminative representations. Furthermore, we propose a residual attention fea-ture group (RAFG) for parallelizing attention and residual block computation. The output of each residual block is linearly fused at the RAFG output to provide access to the whole feature hierarchy. In parallel, DiVA extracts most relevant features from the network for improving the final output and preventing information loss along the successive operations inside the network. Experimental results demonstrate the superiority of DiVANet over the state of the art in several datasets, while maintaining relatively low computation and memory footprint. The code is available at https://github.com/pbehjatii/DiVANet .(c) 2022 Elsevier Ltd. All rights reserved.

Automated summary

This study proposes a computationally efficient and accurate single image super-resolution network called DiVANet. By introducing a directional variance attention mechanism and a residual attention feature group, the network is able to improve the performance and efficiency of image recovery.