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FRR-Net: A Real-Time Blind Face Restoration and Relighting Network
Face restoration models that mitigate low light, mixed lighting, poor camera quality conditions can benefit various applications, including video conferencing, image capture apps, among other uses. Many different models exist to address this problem. Although recent models generate impressive and high-fidelity faces, several important challenges remain, such as model efficiency, realistic texture and facial components, low-light environments, and screen illumination on the face. To tackle these challenges, we propose a simple, yet effective model called Face Restoration and Relighting Network (FRR-Net). The FRR-Net architecture includes an encoder-decoder model with a parallel distortion classifier which predicts the distortion types during training. This model is systematically scaled to balance network depth and width for better performance and efficiency trade-off. In addition, to generate the enhanced facial region, FRR-Net also utilizes a facial segmentation mask during the training, which not only helps the model performance but can also be used for further post-production uses. Furthermore, this work integrates a wide range of data degradation techniques to generate data for training to tackle both face enhancement and relighting. We demonstrate the effectiveness of our method by comparing it with several recent face restoration models. FRR-Net is computationally efficient and can perform inference at 13ms per frame on a low-powered Neural Processing Unit making it suitable for real-time face restoration applications.