DBFSNet: dual-branch frequency and spatial fusion network for real-world social media image super-resolution

The quality of images shared on social media platforms often degrades significantly, leading to diminished visual perception. Although existing real-world super-resolution methods offer satisfactory restoration results, they struggle to strike an optimal balance between restoration performance and inference efficiency. We develop a lightweight dual-branch frequency and spatial fusion network to effectively explore both frequency and spatial features for better image restoration. In addition, we propose a lightweight frequency discriminator network to stabilize the training dynamics. Moreover, we design a synthetic degradation pipeline that simulates the degradation effects commonly existing in social media images, enhancing our ability to tackle real-world challenges. Furthermore, considering the popularity of sharing selfies on social media, we collected a high-quality selfie dataset to support our research efforts. Extensive experimental results demonstrate that our method achieves a better balance between restoration performance and inference efficiency.