GFPGAN is a generative face restoration model and Python-based image processing tool designed to restore low-resolution facial images. It utilizes generative adversarial networks to recover fine details and increase the clarity of degraded portraits.
The system employs a generative facial prior to map degraded images to a high-quality manifold, enabling blind-face restoration without requiring knowledge of the specific degradation process. It utilizes a multi-stage workflow that includes face detection, alignment, and region-specific masking to separate facial areas from the background.
Beyond facial detail recovery, the toolkit includes capabilities for background region enhancement to improve the overall visual quality of a photograph. It also supports the training of specialized restoration models using custom datasets and pre-trained weights.
