Spatial-Frequency Collaborative Network for Efficient Image Super-Resolution

Despite addressing the limitations of CNNs, Transformer-based image super-resolution methods construct long-range features solely in the spatial domain, leading to heavy computational burdens. To tackle this problem, we propose a Spatial-Frequency Collaborative Network(SFCN), which effectively leverages frequency domain information to model non-local dependencies while simultaneously utilizing spatial information to capture local features, thereby achieving superior super-resolution performance. Specifically, we propose a Spatial-Frequency Collaborative Block (SFCB) and a Multi-Scale Gated Feed-Forward Network (MSGFN). The SFCB integrates spatial and frequency domain information, endowing it with both local and non-local feature capture capabilities while maintaining computational efficiency. Furthermore, we introduce the MSGFN to enhance non-linear representation via gating mechanisms and asymmetric convolutions, while capturing multi-scale local details. Extensive experiments demonstrate that our method achieves superior performance while maintaining model complexity comparable to other lightweight methods.