Learning to Super-Resolve Face Images via Dual-Domain Multi-Scale Feature Interaction

Face Super-Resolution (FSR), aiming to improve the quality of Low-Resolution (LR) facial images, has been greatly propelled by the deep learning techniques. However, existing approaches, whether based on Convolutional Neural Networks (CNNs) or Transformers, are either inherently damaging facial structures limited by their architectures or failing to capture essential multi-scale textures due to the rigid receptive fields. To address these concerns, we propose a novel dual-domain feature interaction method called Spatial-frequency Multi-scale feature Learning Network (SMLNet) for FSR by employing a dual-branch architecture. Specifically, the frequency branch captures high-quality global structures and fine high-frequency details, while the spatial branch operates complementarily to preserve fine-grained local texture patterns. Moreover, we further introduce a Multi-scale Spatial-frequency feature Interaction Module (MSIM), which combines a Multi-scale Feature Extraction Block (MFEB) and a Spatial-Frequency feature Interaction Module (SFIM) to interact and aggregate multi-level complementary features from the dual branches. Extensive quantitative experiments and qualitative analyses across multiple datasets, together with evaluations on real-world images, demonstrate that the proposed SMLNet significantly outperforms other state-of-the-art methods.