DFFNet: A Super-Resolution Algorithm based on Dynamic Feature Fusion Network
Jiaqi Yang, Jin Yang, Huiying Jia, Wenguang Zheng · 2025
Image super-resolution is a long-standing challenge in computer vision aimed at reconstructing high-quality images from low-quality images. In recent years, the rapid development of deep learning has driven significant advances in this field, but there are still some limitations of existing methods. They are difficult to take into account the preservation of local details when capturing global information, resulting in overly smooth reconstruction results and insufficient recovery of high-frequency details. And the recovery of high-frequency information is crucial in image reconstruction because it directly determines the perceived quality and visual fidelity of the image. To effectively address these challenges, we propose a dynamic feature fusion network DFFNet for single-image super-resolution to investigate the interaction of local and non-local information. DFFNet introduces non-local information for information interaction based on window self-attention to make up for the defect of information isolation between windows, increase the global feeling field, and realize the effective fusion of local and non-local. Meanwhile, a channel with multi-scale spatial attention module is introduced to further refine the key features and improve the image super-resolution effect. Extensive experimental evaluations show that our network achieves better performance on image super-resolution compared to existing methods.