Edge-Aware Attention Transformer for Image Super-Resolution

In this study, we explore poor edge reconstruction in image super-resolution (SR) tasks, emphasizing the significance of enhancing edge details identified through visual analysis. Existing SR networks typically optimize their network architectures, enabling complete feature extraction from feature maps. This is because the management of spatial and channel information during SR is often pivotal to the network's feature extraction capacity. Despite continuous improvements, directly comparing SR and high-resolution (HR) images through differential mapping reveals the suboptimal performance of these methods in edge reconstruction. In this paper, we introduce a edgey-aware attention transformer (EAT), which focuses on edge reconstruction while maintaining the effective original low frequency information retrieval. Our framework utilizes deformable convolution (DC) to adaptively extract edge features. Then feature enhancement techniques are employed to intensify edge-sensitive features. Furthermore, extensive experiments demonstrate our EAT's exceptional quantitative and visual results, which surpass most benchmarks. This validates the EAT's effectiveness when compared to state-of-the-art models. The code is available at https://github.com/ImWangHaoqian/EAT .