Image upscaling
Also known as: upscale image, enlarge image, AI upscaling, increase image resolution
Image upscaling increases an image’s pixel dimensions by generating new pixels between the originals. Classic methods interpolate from neighboring pixels; AI upscalers predict plausible detail from training data. Neither truly recovers detail the original never captured.
- Adds pixels via interpolation or AI prediction
- Cannot recover detail the original never captured
- Output files are larger than the source image
How upscaling fills in pixels
Traditional upscaling uses interpolation — bilinear or bicubic math that averages surrounding pixels to invent the in-between ones. It enlarges cleanly but tends to look soft, because it adds no information that was not already there.
AI upscalers go further: a model trained on many images predicts what realistic edges, textures, and detail should look like and paints them in. Results can look impressively sharp, but the added detail is a plausible guess, not the real scene.
What it can and cannot do
Upscaling helps when you need a small image to fill a larger frame, smooth out blocky enlargement, or prepare low-resolution art for print. It cannot read a blurred license plate or reconstruct faces that the original pixels never recorded.
Because upscaling adds pixels, the output file is larger than the source. If size matters, follow upscaling with compression or export to an efficient format to keep the file manageable.