Restoring Degraded Face Photos with AI: What Works, What Doesn't, and Why

Restoring Degraded Face Photos with AI: What Works, What Doesn't, and Why Old scanned photos. Blurry screenshots from video calls. Low-resolution profile pictures. Compressed JPEGs that have been resaved thirty times. Face restoration AI handles all of these — but the results vary wildly depending on the input. Here's the honest guide. How GFPGAN Actually Works GFPGAN (Generative Facial Prior GAN) uses a pre-trained face GAN as a prior — essentially a high-quality face generator that gets anchored to your degraded input. Instead of just upsampling pixels, it regenerates the face with realistic texture while staying faithful to the identity. The key advantage over simple upscaling: GFPGAN can add plausible detail that doesn't exist in the original. Skin texture, eye clarity, hair strands — these get reconstructed from the model's learned prior, not copied from surrounding pixels. The key limitation: it's a GAN. It can sometimes "correct" features that look wrong to it but are actually c