Is this a real AI upscaler?

It is honestly described as a "smart" upscaler rather than a neural-network upscaler like Real-ESRGAN. We use multi-pass high-quality resampling (the browser's Lanczos-style scaler) followed by adaptive unsharp masking, which produces visibly sharper results than naive nearest-neighbor or single-pass bicubic. For most photographs, the difference vs. a true ML upscaler is small enough that the privacy and speed trade-offs favour the in-browser approach.

Are my images uploaded?

No — never. Everything runs in your browser using canvas and JavaScript. Open the network tab if you want to verify: zero requests fire when you click Upscale.

What is the maximum image size?

The tool refuses output larger than ~64 megapixels to avoid crashing your browser. For a 4× scale that means source images up to about 4 MP (e.g. 2000 × 2000). On a desktop you can go higher; on mobile, stick to smaller sources.

Why is sharpening adjustable?

Different content needs different sharpening. Crisp text and graphics benefit from 70–90% sharpening; portraits and skin tones look more natural at 40–55%; landscapes are usually best around 60%. The slider applies a capped unsharp mask so you can dial in the right amount for your image.

How does multi-pass upscaling help?

Browsers produce noticeably better results when upscaling in smaller steps. We split 4× into two 2× passes and 3× into a 2× + 1.5× sequence. Each pass uses high-quality resampling and the result is cleaner than a single large jump.

Why does the file size grow so much?

4× upscaling produces 16× the pixel count, which means the encoded file size roughly grows by the same factor for lossless PNG. JPEG output stays smaller because the encoder can compress the smoother upscaled pixels efficiently. Use JPEG for photos, PNG for graphics with sharp edges.

This page handles one image at a time so we can show a focused before/after. For batches, run them sequentially — the algorithm caches no state between runs so quality is identical regardless of order.

Files never leave your device

AI Image Upscaler Online

A free, privacy-first image upscaler that runs entirely in your browser. Uses high-quality multi-pass resampling and adaptive unsharp masking to enlarge photos with cleaner edges than naive scaling. No uploads, no quotas, no watermarks.

Drag and drop a file here

or click to browse

JPG, PNG, or WebP — processed entirely in your browser

Scale

Sharpening: 60%

Adaptive unsharp mask. 60% is a balanced default; raise for crisp text, lower for skin.

How to use Smart Image Upscaler

Drop or browse for a JPG, PNG or WebP image.
Pick a scale: 2×, 3×, or 4×.
Adjust sharpening — 60% is a balanced default.
Click "Upscale" — processing happens locally in your browser.
Download the enlarged image. No watermarks, no upload, no limit.

How the smart image upscaler works

Most free online image upscalers either upload your photo to a server (slow, private data leaves your device) or apply naïve nearest-neighbor scaling that produces jagged, pixelated results. Our upscaler runs entirely in your browser and combines two well-understood techniques to recover detail: multi-pass high-quality resampling and adaptive unsharp masking. The result is visibly sharper than a single-pass canvas resize without requiring a neural-network download.

Multi-pass Lanczos resampling

Browsers expose a high-quality image resampler through the canvas API when imageSmoothingQuality is set to "high". On Chrome and modern Firefox this is a Lanczos-3 implementation; on Safari it's a high-quality bicubic variant. Either way, it's far better than the default bilinear interpolation most pages get.

We split larger scale factors into smaller passes. A 4× upscale becomes two 2× passes; 3× becomes 2× followed by 1.5×. Each pass works on a smoother input, which gives the resampler better data to interpolate. The cost is a tiny amount of extra time; the quality benefit is easy to see on hair, edges, and high-contrast text.

Adaptive unsharp masking

Even the best resampling produces output that looks slightly soft compared to the original — that softness is the price of synthesising pixels that weren't in the source. We counteract it with an unsharp mask: a controlled sharpening filter that enhances edges without amplifying noise.

sharpened = original + amount × (original − blurred)

Where:
  blurred = Gaussian blur of the original
  amount  = sharpening intensity (slider value)

The per-channel change is capped to prevent the bright halos around edges that aggressive sharpening produces. That capping is what makes the result "adaptive" — high- contrast edges get sharpened up to the cap while smooth areas (sky, skin) are left almost untouched.

When to use this vs. a true ML upscaler

Real-ESRGAN, ESRGAN+, and the various commercial upscalers (Topaz Gigapixel, Adobe Super Resolution) use trained neural networks that can hallucinateplausible detail — adding eyelashes, fabric texture, or grass blades that weren't in the source. They produce stunning results on certain images but require:

A 5–80 MB model download (one-time per browser cache).
WebGPU support, which is now widely available but slower on older devices.
Significant CPU/GPU compute — typically 5–60 seconds per image.
Risk of plausible-but-wrong invented detail, especially on faces.

Our upscaler does none of that — instead, it uses well-understood deterministic algorithms that finish in 1–4 seconds, produce honest results (no invented detail), and work on any modern browser without a model download. For typical use cases — making a small photo printable, prepping screenshots for a presentation, refreshing an old image — it's equal or better than ML for everything except faces and fine textures.

Tips for the best results

Start from the highest-quality source you have. Upscaling a heavily compressed JPEG amplifies the artefacts that were already in the file. Start with the original camera roll if possible.
Use 2× when you can, 4× when you must. Each doubling roughly multiplies pixel count by 4 and file size by ~3–4. The marginal quality gain diminishes past 2×.
Crisp content wants more sharpening. Screenshots, text, and line art look best at 70–90% sharpening. Photographs of people sit nicely around 50%.
Crop first, upscale second.If you only need part of the image enlarged, crop the relevant area before upscaling. You'll get more pixels where you need them and avoid wasting processing time on background.

Privacy and limits

Because everything runs locally, there is no quota, no watermark, and no "Pro" version that does something different. Your image bytes never leave your device — open the network tab in DevTools to confirm. The only ceiling is your browser's available memory; we cap output at 64 megapixels to prevent tab crashes on mobile.

Related image tools

AI Background Remover — strip backgrounds with a true on-device neural network.
Image Compressor — shrink JPGs and PNGs after upscaling so the file is web-ready.
Image Resizer — set exact pixel dimensions instead of using a scale factor.
Image Cropper — crop before upscaling for sharper detail where it matters.