What Is Image Upscaling?

Traditional vs AI Upscaling

How AI Upscaling Works

AI super-resolution models are trained on pairs of images: a high-resolution original and a synthetically degraded low-resolution version. The model learns to reverse the degradation process — given a low-resolution input, it predicts what the high-resolution version should look like.

The most widely used model for general image upscaling is Real-ESRGAN, which handles real-world degradations (blur, noise, JPEG artifacts, and resolution loss simultaneously). For faces specifically, GFPGAN produces superior results because it uses face-specific training data and generative priors.

Leading Upscaling Models

• → Real-ESRGAN — General-purpose upscaling model that handles real-world degradations. Supports 2x and 4x scales. Works on any image type.
• → SwinIR — Transformer-based model with state-of-the-art PSNR scores. More computationally expensive but produces very clean results.
• → Stable Diffusion Upscaler — Uses diffusion models for upscaling. Can add creative detail but may alter the image more than other methods.
• → GFPGAN — Not a general upscaler, but the best option for face-specific restoration and enhancement.

Upscaling vs Face Restoration

For old portrait photos, the question is often whether to use a general upscaler or a face restoration model. The answer depends on what you need:

• → Faces are the priority — Use face restoration (GFPGAN). It produces dramatically better facial results than any general upscaler.
• → Whole image needs enlarging — Use a general upscaler (Real-ESRGAN) to increase resolution across the entire image.
• → Both needed — Run face restoration first, then upscale the result. This gives the best of both worlds.