greycard denoise
Raw-domain denoisers for greycard,
a RAW development engine and editor. The editor and the greycard CLI download
these on first use; nothing here needs to be fetched by hand.
Three tiers, one UNet architecture at three widths. Every one of them is a single ONNX file (opset 17), fp32.
| Tier | File | Parameters | Network time, 24 MP frame |
|---|---|---|---|
| fast | denoise-v19.onnx | 1.2 M | ~1 s |
| balanced | denoise-v15.onnx | 2.3 M | ~2 s |
| best | denoise-v20.onnx | 5.3 M | ~3 s |
Times are the network alone on the reference hardware; greycard tiles and blends the answer, and the whole develop takes longer.
Contract
- Input
packed:1 × 4 × h × w, float32. The Bayer mosaic packed to half resolution as R, G1, G2, B, after black and white level normalisation and white balance, each channel through greycard's variance stabiliser (2x / (sqrt(a·x + s0²) + s0)with the frame's measured noise parameters). Height and width are dynamic. - Output
rgb:1 × 3 × 2h × 2w, float32. Demosaiced RGB at the mosaic's resolution, in the same stabilised space, camera RGB, white balanced. greycard inverts the stabiliser and carries on with its usual pipeline.
The network is not a general-purpose image denoiser: it expects stabilised, white-balanced camera-space mosaic data and will do the wrong thing on sRGB pictures.
Training
Trained by the greycard author on frames from their own archive, with noise
synthesised from greycard's per-frame noise model (photon plus read noise,
sampled log-uniformly over a wide range, with per-channel gain jitter).
L1 loss in the stabilised space, EMA weights. The code is in the greycard
repository under tools/denoise (train.py, model.py, export.py).
Licence
GPL-3.0-or-later, the same as greycard. Source and training code: https://github.com/jessolmstead/greycard.