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AI Image Upscaler

Upscale images 2x or 4x in your browser using high-quality interpolation. Optional ONNX AI mode (beta).

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About this tool

AI Image Upscaler enlarges your photos and images by 2× or 4× directly in your browser. The fast mode uses high-quality bicubic canvas interpolation — the sharpest non-AI method available in-browser — and works instantly on any device. An optional AI (beta) mode attempts to load an ONNX-based upscaling model; if the model is unavailable it transparently falls back to the bicubic path. Both modes output a full-resolution PNG you can download immediately. No server, no upload, no watermark.

Why use it

  • Works entirely in-browser — no upload, no privacy risk.

  • Fast bicubic mode runs instantly on any hardware.

  • Side-by-side comparison shows original and upscaled dimensions.

  • No watermarks, no sign-up, completely free.

  • Outputs a lossless PNG regardless of input format.

  • Works entirely in-browser via Canvas2D and ONNX Runtime Web — no upload, no privacy risk.

How to use

  1. Upload a JPG, PNG, or WebP image using the file input or drag-and-drop area.
  2. Choose a scale factor: 2× doubles the dimensions, 4× quadruples them.
  3. Select 'Fast (bicubic)' for instant results or 'AI model (beta)' for experimental AI upscaling.
  4. Click 'Upscale Image' and view the side-by-side comparison.
  5. Click 'Download PNG' to save the enlarged image.
  6. Choose a scale factor: 2× doubles the dimensions (4× the pixels), 4× quadruples them (16× the pixels).

When it helps

  • Printing a small digital image at a larger size for posters or framed prints.
  • Enlarging a low-resolution photo for a presentation slide or video thumbnail.
  • Preparing a thumbnail image for use as a banner or cover.
  • Testing how an image looks at higher resolution before sending to a printer.

Examples

Old screenshot

Input

PNG screenshot at 640 × 480 px

Expected result

Lossless PNG at 2560 × 1920 px (4×) suitable for embedding in a presentation.

Photo for print

Input

JPG portrait at 1024 × 768 px

Expected result

PNG at 2048 × 1536 px (2×) sharp enough for a 6×4 print at 300 DPI.

Thumbnail to banner

Input

WebP avatar at 256 × 256 px

Expected result

PNG at 1024 × 1024 px (4×) usable as a profile banner or large social card.

Tips

  • Use 2× first; only escalate to 4× if the result still looks too small — quality drops with each doubling and 4× generates 16× more pixels.
  • AI mode shines on photos with clear edges and limited noise; if your source has heavy JPEG artefacts, denoise first or stick with bicubic.
  • If your browser runs out of memory on 4× of a 4000 px image, downscale slightly first — 16000 × 16000 PNG can exceed 200 MB in RAM.
  • Always download immediately; canvas blobs are garbage-collected and switching tabs may discard the upscaled output.
  • For pixel art, this tool is the wrong choice — use a nearest-neighbour zoom in your editor to keep crisp pixel boundaries.
  • Compare both modes on a sample image before committing to a batch — bicubic is sometimes preferable for posterised illustrations even when AI is available.

Frequently Asked Questions

What is the difference between fast and AI mode?
Fast mode uses the browser's built-in bicubic interpolation at maximum quality — it is sharp and instant. AI mode attempts to load an ONNX neural-network upscaler; if the model is unavailable it falls back to fast mode.
Will upscaling add detail that was not there?
Fast (bicubic) mode enlarges existing pixels smoothly but does not invent new detail. AI mode attempts to reconstruct plausible detail, but results vary by image type.
Are there any file size limits?
Very large images may strain browser memory. Images above 4000 × 4000 px at 4× scale (→ 16000 × 16000) may be slow or fail on low-RAM devices.
Does it upload my image?
No. Everything runs client-side in your browser. Your image is never sent to any server.
What output format is produced?
A lossless PNG regardless of the input format.

Explore the category

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How AI Image Upscaling Beats Bicubic Interpolation Old image upscaling guesses pixels with smooth math. AI upscalers like ESRGAN and Real-ESRGAN hallucinate plausible detail learned from mill Mar 2026 · 9 min How AI Background Removal Actually Works From U-Net segmentation to modern transformer-based matting, we walk through how a single neural net can isolate a foreground subject in a p Mar 2026 · 9 min Image Formats Explained: JPEG, PNG, WebP, AVIF, GIF Compared Choosing the wrong image format can double your page weight or break compatibility in older browsers. This guide cuts through the noise with Apr 2026 · 9 min

Glossary

Bicubic interpolation
An image-resampling method that fits a smooth cubic curve through 4×4 neighbourhoods of source pixels, producing sharper enlargements than bilinear or nearest-neighbour.
Super-resolution
The class of algorithms that infer plausible missing detail when enlarging an image, typically via convolutional networks trained on paired low/high-resolution images.
ONNX
Open Neural Network Exchange format for portable AI models; the upscaler ships an ONNX file that ONNX Runtime Web can execute in the browser.
WebAssembly (Wasm)
A portable bytecode that runs near-native code inside browsers; ONNX Runtime Web compiles to Wasm so the AI model runs without a server.
IndexedDB
A persistent client-side database used to cache the ONNX model so the 25–60 MB download only happens once per browser profile.
Bilinear vs bicubic
Bilinear blends 2×2 source pixels and is fast but soft; bicubic uses 4×4 with cubic curves and gives noticeably sharper edges, at slightly higher cost.
Aliasing
Stair-stepped artefacts that appear when an image is resized without proper filtering; bicubic and AI methods both reduce visible aliasing.