How AI upscaling works

AI upscalers are trained on low/high image pairs to predict plausible high-frequency detail. Diffusion models generate it; interpolation only averages. Detail is predicted, not recovered.

Trained to predict detail

An upscaler is trained on pairs: the same image at low and high resolution. The model learns the mapping - given a blurry edge, what sharp edge usually follows.

Classic interpolation (bicubic, Lanczos) averages neighboring pixels. It sharpens nothing and invents nothing.

Diffusion-based upscalers go further: they regenerate the image at high resolution, hallucinating texture that fits the scene. Skin pores, fabric weave, leaf edges. Usually plausible, occasionally wrong - which is why you compare against the original before shipping.