Image Enlarger — Free, Fast & Private
An image enlarger has one job: take a photo that is too small for what you need — a print, a frame, a poster, a canvas — and make it genuinely bigger without the blocky, jagged look that ordinary scaling produces. MiniPx does this with a super-resolution neural network (realesr-general-x4v3 from the Real-ESRGAN project) that runs inside your own browser. The 4.9 MB model is fetched once, cached, and from then on every enlargement happens on your device. No photo you enlarge here is ever transmitted anywhere.
Why stretched photos pixelate. When you scale an image up in a basic editor, the software has to invent new pixels from old ones, and classical interpolation invents them crudely: nearest-neighbour copies each pixel into a block (the staircase look), while bicubic averages neighbours into a haze (the soft look). Either way, one pixel of source data gets smeared across sixteen output pixels at 4×, and your eye reads the smearing instantly. A trained network approaches the same problem with learned knowledge of what photographs look like — it has seen how sharp edges, skin, brick, grass, and lettering degrade when shrunk, and it reverses that degradation plausibly instead of arithmetically. The difference is most dramatic exactly where stretching fails worst: hard edges, fine texture, and diagonal lines.
The print math, worked out. Print sharpness is a simple division: pixels ÷ DPI = inches. A framed photo viewed up close wants 300 DPI. So a 1080px-wide phone photo prints only 3.6 inches wide before it visibly degrades — but enlarged 4× to 4320px, the same photo covers 14.4 inches at full 300 DPI, comfortably past A4 and into 12×18 territory. Distance buys you more: posters read fine at 150 DPI (that 4320px file now spans 28.8 inches), and canvas texture hides detail so well that 100-125 DPI passes on a gallery wrap. Working backwards: an 8×10 at 300 DPI needs 2400×3000px, reachable from any source of at least 600×750. If you are unsure what your file can support, the DPI checker reads your image and does this arithmetic for you.
Old photos, scans, and small keepsakes. The most satisfying enlargements are usually rescues: the 640×480 file from a 2003 digital camera, the low-resolution copy that survived a phone migration, the photo a relative sent through a chat app that recompressed it to nothing, the small scan of a print that has since been lost. These sources respond well because they are typically sharp — just small — which is precisely the case learned synthesis handles best. One honest rule before enlarging a scan: if the physical original still exists, rescan it at 600 DPI instead. A scanner captures real detail; the network only ever proposes likely detail. Reach for the enlarger when the small digital file is all that remains.
Enlarging is not resizing. A resizer such as the MiniPx resizer is built for the downward direction — trimming a 4000px photo to 1280px for the web loses nothing you can see and is the right move for uploads and email. The upward direction is a different problem entirely, and it is the one this page exists for. Rule of thumb: shrinking is a resize job, growing by more than about 20% is an enlarger job, and in the narrow band between, either tool looks the same. The two are complementary steps in one workflow, not competitors.
Where enlargement honestly fails. Some sources are beyond any enlarger, and it is better to know before you order the print. Tiny thumbnails — say a 150px avatar — carry so little information that even a perfect 4× result is a 600px image built mostly from guesswork; faces come back smooth and subtly wrong. Heavily compressed JPEGs are the second trap: the network struggles to distinguish compression debris from genuine fine texture, so aggressive artifacts can come back enlarged and sharpened rather than removed. And focus problems do not improve with size — an out-of-focus subject enlarges into a bigger out-of-focus subject. If a source is both tiny and heavily compressed, expect a result that looks acceptable at a glance and artificial under inspection.
What the free tier gives you, exactly. Free enlargement accepts sources up to 1080px on the long edge and outputs up to 4320px at 4×, with a small "minipx.com" watermark in one corner. That output size already covers most framing and poster uses, as the arithmetic above shows. Pro removes the watermark and lifts the input ceiling to 2048px for 4× or 4096px for 2×, both delivering up to 8192px — large-format territory. Oversized sources are scaled down to the cap before enlargement, and the tool says so rather than surprising you in the output. Only two factors are offered, 2× and 4×; there is no 8× option, because stacking that much synthesis produces obviously artificial texture.
Nothing to upload means nothing to leak. Family photos, children, old documents — enlargement requests are often the most personal images people handle. Here the model travels to you and your photo stays put: inference runs through WebGPU (or WebAssembly on older machines), and after the first visit the cached model works with the network cable unplugged. You can confirm the absence of uploads yourself in the browser's network panel. There is no account, no queue, and no image retention because there is no server receiving images in the first place.
Order of operations for a print job: crop to your frame's aspect ratio first with the cropper, enlarge second, and only compress last — and only if the print shop wants a smaller upload, using the quality-preserving compressor. Enlarging a cropped image spends every synthesised pixel inside the frame instead of on areas you will discard. For screen-resolution targets rather than print — 4K wallpapers and displays — the dedicated upscale to 4K page walks the resolution math, and the full engine documentation lives on the AI image upscaler hub.