dejpeg

JPEG artifact removal that runs entirely in your browser. Photos never leave the device: inference runs client-side via ONNX Runtime Web (WebGPU primary, WASM fallback), backed by a compact custom-trained model.

Try it: https://dejpeg.ludwina.top

What's here

Path	What
apps/web/	Astro + Svelte 5 web app (the tool itself)
packages/inference-core/	Framework-free TypeScript inference library: worker, codecs, adaptive tiling
packages/fbcnn-py/	Modern PyTorch port of FBCNN
packages/dejpeg-train/	Research training pipeline: WebDataset corpora, synthetic degradation, dedup manifest
tools/convert-fbcnn/	FBCNN PyTorch → ONNX FP16 conversion tool
models/dejpeg/	DeJPEGNet: the shipped model (training, inference, ONNX export; weights via GitHub Releases)

Quickstart

Web app (bun):

bun install
bun run dev          # http://localhost:4321/tool

Python packages (uv):

uv sync                       # fbcnn-py + tools (workspace)
pip install -e models/dejpeg  # shipped model package

Model inference:

from dejpeg import load_model, restore_image

model = load_model()                  # release weights (auto-downloaded to ~/.cache/dejpeg)
restore_image("input.jpg", "out.png", model)

Training from your own images:

python models/dejpeg/scripts/train.py --data ~/datasets/clean-images --out runs/v1

The full research pipeline (packages/dejpeg-train/) builds WebDataset shard corpora from DIV2K/Flickr2K-class sources and synthesizes JPEG degradations on the fly; see its README.

Development

bun test               # inference-core unit tests
uv run pytest packages/fbcnn-py
pytest models/dejpeg   # model architecture/inference tests

License

Apache-2.0. FBCNN-derived code (packages/fbcnn-py/) originates from an Apache-2.0 upstream. Benchmark images (Classic5, LIVE1) are not distributed.