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Add dr-denoise: the learned demosaic and denoise, without the UI

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The noise model takes the best source the frame has: the body's measured
table (the Canon EOS 6D's, from the library), the DNG's NoiseProfile, or
the frame itself — read, row and column noise from its masked border, and
only the shot gain estimated, from the quietest flat patches. Checked on
130 6D frames, the estimate is within 10 % from ISO 1000 up; the network
loses under 0.3 dB for a sigma off by 15-20 %, so every Bayer body is
eligible.

Tiles of 1408 keep their central 1024 behind a 192-photosite halo, past the
185-photosite receptive field, and the frame is extended by reflection,
which keeps every photosite's colour; a pattern that starts on another
colour is read from one photosite up or left so the network sees RGGB, and
nothing is cropped. The tests run every Bayer phase, tiled against whole,
with a stand-in network of known reach.

The model ships as models/denoise/mosaic-1408.onnx (LFS), trained in
darkroom-denoise on the maintainer's own photographs, GPL like the code.
denoise_raw runs a file end to end: on a 6D frame at ISO 8000 the result
matches the training repository's own path to 2.5e-4 at worst, and takes
3.1 s on TensorRT fp16 (75 dB from f32) or 14.4 s on the CPU.
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dtourolle
2026-10-03 11:15:50 -04:00
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models/LICENCE.md
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@@ -122,3 +122,16 @@ position of any model here. The training set is Places2, a research dataset,
but the weights are released under the repository's licence without a
data-derived restriction (contrast the gaze models §7 of the requirements
declined, and the InsightFace grant of D13).
## `denoise/` — the mosaic denoiser, the project's own
| File | Source | Trained on | Used by |
|---|---|---|---|
| `denoise/mosaic-1408.onnx` | trained from scratch in the `darkroom-denoise` repository (2026-10-03, run `m2`, 60 000 steps) | 427 of the maintainer's own base-ISO Canon EOS 6D raws, with the 6D's measured noise added | the learned demosaic and denoise (FR-DEV-3g) |
A U-Net of plain 3×3 convolutions, ReLU, strided and transposed
convolutions and additive skips — no third-party architecture code or
weights — at a fixed `1×1×1408×1408` for `mosaic` and `sigma`, exported by
`python -m denoise.export` in `darkroom-denoise`. Trained only on
photographs the maintainer owns, so the weights carry no grant but the
project's own: GPL-3.0-or-later, like the code (denoise.md §10).