A Bayer photograph keeps its mosaic in the session and is offered the AI
Denoise switch. Asked for, the network runs on the decode executor from a
hot-pixel-repaired copy — the app's own pass — with the frame's noise from
its best source, and its progress in the activity bar; the classical
demosaic shows until the result lands, and the finished job says where the
noise figures came from. Keep grain is a GrainBlend of the two, made once
per value; the render draws it as its source and the adjust pass never
knows. demosaiced stays the classical result, so the raw histogram, the
white balance picker, masks and segmentation still read the sensor.
The develop view reconciles on a 250 ms poll rather than on each way an
edit can change (slider, undo, preset, version, a sidecar from another
device): two comparisons when nothing changed, and no path that can forget.
A failure is not retried until the switch is toggled. An export of a
photograph that asks for it waits for a running job or computes it.
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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