AI Image Upscaler - Super Resolution
How neural upscaling differs from resizing
Enlarging an image the ordinary way is guesswork by arithmetic. Bicubic and similar methods look at the pixels surrounding each new one and average them, which is why a stretched photo comes out soft: no new information was added, the existing information was just spread over a larger area.
Super-resolution takes a different route. The model here is ESRGAN-slim, running through TensorFlow.js, and it was trained on a very large number of paired low and high resolution images until it learned what kinds of fine structure typically correspond to a given blurry patch. Given a soft edge it reconstructs a sharp one; given a smudge of texture it reconstructs plausible grain. The output is a 2x enlargement that genuinely looks detailed rather than merely bigger.
That makes it well suited to old scans and family photos taken on early digital cameras, small product images that need to fill a modern layout, logos and graphics recovered at the wrong size, and screenshots destined for a high-density display. Sharp graphic content with clean edges tends to come out especially well, as does natural texture like fabric, foliage and stone.
The whole model runs on your own machine through WebGL, so the first run pauses briefly to download the weights and then works offline from cache. Nothing is uploaded, there are no credits to buy, no watermark on the output and no cap on how many images you enhance, which is unusual for this category since most AI upscalers meter access precisely because they are paying for server GPUs.
How to upscale an image
- Upload the image. PNG, JPG and WebP all work. Start from the cleanest copy you have, since the model reconstructs from what is actually there.
- Let the model load. The first run downloads the network weights, which takes a few seconds. After that it is cached and starts immediately.
- Click Run AI Upscaling. Processing happens on your GPU through WebGL. Larger images take proportionally longer.
- Compare, then download. Check the side-by-side view, then save the 2x result at full quality with no watermark.
For more than 2x, run the output back through a second time, each pass doubles again. If the source is grainy, clean it first with the AI denoiser, since removing noise before upscaling gives the model clearer structure to work from. See also how to upscale images with AI.
Frequently asked questions
How is this different from just resizing?
Resizing averages the pixels you already have, which spreads the same information over a larger area and looks soft. Super-resolution reconstructs detail it learned from training data, so edges come back sharp and texture comes back as texture.
How much bigger does it make the image?
2x per pass, so 1000px becomes 2000px. Run the result through again for 4x. Each pass adds processing time, so step up gradually rather than expecting one click to reach print size.
Which images upscale best?
Clean sources with real structure: graphics with defined edges, and natural texture like fabric, foliage or stone. Denoising a grainy photo first with the AI denoiser gives the model clearer structure to work from.
Why does the first run take a moment?
It downloads the neural network weights once. After that the model is cached in your browser and subsequent runs start immediately, even offline.
Are there credits, limits or watermarks?
None. Because the model runs on your hardware rather than a rented GPU, there is nothing to meter. Upscale as many images as you like.
Are my photos uploaded?
No. The model runs in your browser via WebGL, so old family photographs and client work are enhanced without leaving your device.
🔎 Verify it yourself: nothing uploads
Don’t take our word for it. Open your browser’s DevTools (F12 → Network tab), then run this tool on any image. The only download you’ll see is the open-source AI model coming to your browser — your image itself is never sent anywhere. Every tool on this site works the same way — all processing happens on your device.