A dual branch attention network based on practical degradation model for face super resolution

In recent years, the field of face super-resolution (FSR) has advanced rapidly. However, complex degradation factors in real-world scenarios can severely deteriorate image quality, significantly affecting the reconstruction performance of FSR methods. Currently, there is a lack of research on degradation modeling for real-world facial images, which impacts the generalization ability of existing FSR methods. In this paper, a practical degradation model based on hybrid degradation processes is proposed to select multiple degradation processes including Gaussian noise, Rayleigh noise, Motion blur, Salt-and-Pepper noise, and Mean blur through a stochastic strategy to more realistically simulate the effect of image distortion in real scenarios. We also design a dual-branch attention network called DBANet for face super-resolution and conduct experiments on the SCUT_FBP, Helen and PFHQ datasets, achieving satisfactory results. Our proposed model is effective in handling image distortion under different degradation modalities, which improves the robustness of super-resolution reconstruction. This study introduces an innovative approach to the field of face image super-resolution, which has the potential for a wide range of practical applications. The code of DBANet will be available at https://github.com/bxzha/DBANet.