A Component-Based Hybrid GAN architecture designed to restore high-fidelity facial features (eyes, nose, mouth) in images degraded by social media compression, motion blur, and sensor noise.
The primary objective is to achieve perceptual realism while strictly preserving the subject's unique identity. This model leverages specialized discriminators for local facial components and an identity-consistency module.
The model was trained using paired training data generated via synthetic distortion from:
Images on social media often suffer from aggressive lossy compression. This project implements a Hybrid GAN featuring:
1import torch
2# Assuming your model architecture class 'Generator' is defined
3model = Generator()
4model.load_state_dict(torch.load("socialface_restore_gan.pth", map_location="cpu"))
5model.eval()
