ESRGAN deep learning via upscaler.js + WebAssembly — 2× super-resolution running privately in your browser.
ESRGAN Neural Network
Enhanced Super-Resolution GAN — a deep learning model that reconstructs missing detail rather than just stretching pixels.
WebAssembly Runtime
Upscaler.js with WASM backend runs ESRGAN directly in the browser — no GPU, no server, no uploads.
AI upscaling pipeline
1
Image Load
Decode & preprocess
2
Patch Split
Divide into tiles
3
Feature Extract
RRDB analysis
4
Detail Generate
AI upscaling
5
Patch Merge
Seamless blend
6
Export
PNG/WebP/JPEG
Upscaling methods comparison
| Method | Technology | Quality | Speed | Best for |
|---|---|---|---|---|
| Bicubic | Interpolation | ★★ | Instant | Simple scaling, no detail needed |
| Lanczos | Advanced interpolation | ★★★ | Instant | Quick resizing, sharper than bicubic |
| ESRGAN (imgshrnk) | Deep Learning + WASM | ★★★★★ | 5–60 s | High-quality enhancement with detail reconstruction |
| ESRGAN (GPU) | Deep Learning + CUDA | ★★★★★ | 1–10 s | Fastest AI upscaling (requires powerful GPU) |
Technical specifications
Use cases
Photo Enlargement
Prepare photos for large-format printing or professional displays.
Retina Displays
Create sharp graphics for high-DPI screens and responsive web design.
Digital Art
Scale artwork and illustrations for different display sizes.
Product Photos
Enhance e-commerce images for zoom features and detail views.
Social Media
Upscale images for Instagram, LinkedIn, and Twitter with enhanced details.
Video Thumbnails
Create high-quality thumbnails from video frames for YouTube and Vimeo.
ESRGAN, not bicubic
Unlike simple interpolation, ESRGAN reconstructs plausible detail from learned patterns — the difference is visible at high zoom.
No GPU required
ESRGAN runs via WebAssembly on any modern CPU. Slower than a dedicated GPU but private, free, and with zero setup.
~5 MB model
The ESRGAN-thick model is downloaded once and cached. All subsequent upscales are instant to start.