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

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.

Cargo.lock

@@ -1498,6 +1498,23 @@ dependencies = [
  "zune-jpeg 0.4.21",
 ]
 
+[[package]]
+name = "dr-denoise"
+version = "0.20.0"
+dependencies = [
+ "dr-decode",
+ "dr-gpu",
+ "dr-inference-engine",
+ "env_logger",
+ "log",
+ "ndarray",
+ "ort",
+ "pollster",
+ "serde",
+ "serde_norway",
+ "thiserror 2.0.20",
+]
+
 [[package]]
 name = "dr-export"
 version = "0.20.0"

Cargo.toml

@@ -5,6 +5,7 @@ members = [
     "core/dr-catalog",
     "core/dr-thumbs",
     "core/dr-decode",
+    "core/dr-denoise",
     "core/dr-export",
     "core/dr-face",
     "core/dr-film",
@@ -44,6 +45,7 @@ dr-types = { path = "core/dr-types" }
 dr-catalog = { path = "core/dr-catalog" }
 dr-thumbs = { path = "core/dr-thumbs" }
 dr-decode = { path = "core/dr-decode" }
+dr-denoise = { path = "core/dr-denoise" }
 dr-export = { path = "core/dr-export" }
 # Stated explicitly for the same reason as `dr-segment` below: no dependant
 # should drag in an ONNX runtime by accident. Members opt in with

core/dr-decode/src/profile.rs

@@ -757,7 +757,7 @@ pub fn read_noise_profile(decoder: &dyn rawler::decoders::Decoder) -> Option<Vec
     let ifd = decoder.ifd(WellKnownIFD::Root).ok()??;
     let entry = ifd.get_entry_recursive(DngTag::NoiseProfile)?;
     let n = entry.count() as usize;
-    if n < 2 || n % 2 != 0 {
+    if n < 2 || !n.is_multiple_of(2) {
         return None;
     }
     let pairs: Vec<(f32, f32)> = (0..n / 2)

core/dr-denoise/Cargo.toml

@@ -0,0 +1,32 @@
+[package]
+name = "dr-denoise"
+version.workspace = true
+edition.workspace = true
+rust-version.workspace = true
+license.workspace = true
+
+[dependencies]
+dr-decode.workspace = true
+serde = { workspace = true }
+serde_norway.workspace = true
+thiserror.workspace = true
+log.workspace = true
+
+# The network runs under the inference engine like every other model
+# (docs/dev/inference.md): `ort` is the API, the engine picks the rung.
+# Optional so the noise model and the tiling test without a runtime.
+ort = { workspace = true, optional = true }
+dr-inference-engine = { workspace = true, optional = true }
+ndarray = { workspace = true, optional = true }
+
+[features]
+default = ["onnx"]
+onnx = ["dep:ort", "dep:dr-inference-engine", "dep:ndarray"]
+# A real ONNX Runtime from disk rather than tract alone, as the app links it.
+native = ["onnx", "dr-inference-engine/native"]
+
+[dev-dependencies]
+# The example repairs hot photosites with the app's own pass, as develop will.
+dr-gpu.workspace = true
+pollster.workspace = true
+env_logger.workspace = true

core/dr-denoise/examples/denoise_raw.rs

@@ -0,0 +1,132 @@
+//! Denoise one RAW file end to end, as develop will, and time it.
+//!
+//! ```sh
+//! DARKROOM_ORT_DIR=~/.local/share/darkroom/runtime \
+//!     cargo run --release -p dr-denoise --features native --example denoise_raw -- IMG.CR2 out
+//! ```
+//!
+//! Decode, the app's hot-pixel pass, the frame's noise from its best source,
+//! then the shipped network under the inference engine on whatever rung this
+//! machine probes to. Writes `out.npy` — the active area, `h×w×3` f32 linear
+//! camera RGB — for comparison with the training repo's own path
+//! (`tools/compare_rust.py` in darkroom-denoise). `DARKROOM_ORT_DIR` points
+//! at an ONNX Runtime build; the engine's cache goes to `DR_ENGINE_CACHE` or
+//! a temporary directory.
+
+use std::path::PathBuf;
+use std::time::{Duration, Instant};
+
+use dr_denoise::onnx::OnnxNet;
+use dr_inference_engine::{Config, Role};
+
+fn main() {
+    env_logger::Builder::from_env(env_logger::Env::default().default_filter_or("warn")).init();
+    let mut args = std::env::args().skip(1);
+    let (Some(input), Some(out)) = (args.next(), args.next()) else {
+        eprintln!("usage: denoise_raw RAW OUT_PREFIX");
+        std::process::exit(2);
+    };
+    let model =
+        PathBuf::from(env!("CARGO_MANIFEST_DIR")).join("../../models/denoise/mosaic-1408.onnx");
+    let cache = std::env::var_os("DR_ENGINE_CACHE")
+        .map(PathBuf::from)
+        .unwrap_or_else(|| std::env::temp_dir().join("dr-denoise-engines"));
+    let started = Instant::now();
+    dr_inference_engine::init(Config {
+        runtime_dirs: std::env::var_os("DARKROOM_ORT_DIR")
+            .map(PathBuf::from)
+            .into_iter()
+            .collect(),
+        cache_dir: cache,
+        models: vec![(Role::Denoiser, model.clone())],
+        embedded: Vec::new(),
+        ceiling: None,
+        threads: 0,
+        decay: Duration::ZERO,
+    });
+    // Wait for the probe and the engine build, so the timing below is the
+    // rung this machine settles on, not the fallback used while it compiles.
+    // The probe starts on its own thread; give it a moment to say so.
+    std::thread::sleep(Duration::from_secs(1));
+    loop {
+        let s = dr_inference_engine::status();
+        if !s.probing && s.engines.0 >= s.engines.1 {
+            println!(
+                "engine        {} ({:.1} s to settle)",
+                s.line(),
+                started.elapsed().as_secs_f64()
+            );
+            break;
+        }
+        std::thread::sleep(Duration::from_millis(200));
+    }
+
+    let bytes = std::fs::read(&input).expect("read raw");
+    let t = Instant::now();
+    let mut raw = dr_decode::decode(&bytes).expect("decode");
+    let meta = dr_decode::metadata(&bytes).expect("metadata");
+    let decode = t.elapsed();
+
+    let t = Instant::now();
+    let ctx =
+        pollster::block_on(dr_gpu::GpuContext::new_headless()).expect("GPU for the hot-pixel pass");
+    let repaired = dr_gpu::Demosaicer::new(&ctx)
+        .expect("demosaicer")
+        .repair_hot_pixels(&mut raw)
+        .expect("repair");
+    let repair = t.elapsed();
+
+    let noise = dr_denoise::noise::for_frame(&raw, &bytes, meta.iso)
+        .expect("no noise source for this frame");
+    println!(
+        "frame         {} {} ISO {:?}, {}×{}, {:?}, {repaired} hot photosites repaired",
+        raw.make, raw.model, meta.iso, raw.crop.width, raw.crop.height, raw.cfa_pattern
+    );
+    println!(
+        "noise         {} — σ at 10 % grey (G) {:.5}, read {:.5}, row {:.5}, col {:.5}",
+        noise.source.label(),
+        noise.sigma(1, 0.1),
+        noise.o[1].sqrt(),
+        noise.row,
+        noise.col
+    );
+
+    let mut net = OnnxNet::from_path(&model).expect("model");
+    println!(
+        "rung          {}",
+        net.rung().map(|r| r.label()).unwrap_or("?")
+    );
+    let t = Instant::now();
+    let rgb = dr_denoise::denoise(&raw, &noise, &mut net, &mut |done, total| {
+        eprint!("\rtile          {done}/{total}");
+        true
+    })
+    .expect("denoise")
+    .expect("not cancelled");
+    let run = t.elapsed();
+    eprintln!();
+    println!(
+        "time          decode {:.2} s · hot pixels {:.2} s · network {:.2} s ({:.1} MP)",
+        decode.as_secs_f64(),
+        repair.as_secs_f64(),
+        run.as_secs_f64(),
+        (raw.crop.width * raw.crop.height) as f64 / 1e6
+    );
+
+    let (h, w) = (raw.crop.height as usize, raw.crop.width as usize);
+    let mut npy = Vec::with_capacity(rgb.len() * 4 + 128);
+    let mut header =
+        format!("{{'descr': '<f4', 'fortran_order': False, 'shape': ({h}, {w}, 3), }}");
+    while (10 + header.len() + 1) % 64 != 0 {
+        header.push(' ');
+    }
+    header.push('\n');
+    npy.extend_from_slice(b"\x93NUMPY\x01\x00");
+    npy.extend_from_slice(&(header.len() as u16).to_le_bytes());
+    npy.extend_from_slice(header.as_bytes());
+    for v in &rgb {
+        npy.extend_from_slice(&v.to_le_bytes());
+    }
+    std::fs::write(format!("{out}.npy"), npy).expect("write");
+    println!("wrote         {out}.npy");
+}

core/dr-denoise/src/lib.rs

@@ -0,0 +1,79 @@
+//! TRACES: FR-DEV-3g
+//! Learned demosaic and denoise on the raw mosaic (docs/dev/denoise.md).
+//!
+//! A network trained on the library's own base-ISO raws with the 6D's
+//! measured noise added takes the repaired, normalised mosaic and a σ for
+//! every photosite, and returns linear camera RGB at full resolution — the
+//! texture the classical demosaic would have produced, with the noise gone.
+//! It replaces the demosaic box; nothing downstream changes (§2).
+//!
+//! - [`noise`] says how noisy each photosite is, from the best source the
+//!   frame has.
+//! - [`tile`] runs a fixed-shape network over a whole frame, exactly.
+//! - [`onnx`] is that network under the inference engine.
+//!
+//! The input must already have been through the app's hot-pixel pass
+//! (`dr_gpu::Demosaicer::repair_hot_pixels`): the noise model was fitted
+//! with what that pass removes left out.
+
+pub mod noise;
+#[cfg(feature = "onnx")]
+pub mod onnx;
+pub mod tile;
+
+use dr_decode::RawImage;
+
+pub use noise::{NoiseModel, Source};
+pub use tile::{TileNet, HALO};
+
+#[derive(Debug, thiserror::Error)]
+pub enum DenoiseError {
+    #[error("the network cannot take this photograph: {0}")]
+    Unsupported(String),
+    #[error("the denoise model misbehaved: {0}")]
+    Model(String),
+    #[error("could not read the denoise model: {0}")]
+    ModelRead(#[from] std::io::Error),
+    #[cfg(feature = "onnx")]
+    #[error(transparent)]
+    Engine(#[from] dr_inference_engine::Error),
+    #[cfg(feature = "onnx")]
+    #[error(transparent)]
+    Ort(#[from] ort::Error),
+}
+
+/// Whether the learned stage can take this frame at all: a Bayer mosaic.
+/// X-Trans needs its own model (§9); a linear DNG has no photosites.
+pub fn eligible(raw: &RawImage) -> bool {
+    raw.samples_per_pixel == 1 && tile::rggb_offset(raw.cfa_pattern).is_some()
+}
+
+/// The active area of `raw`, denoised and demosaiced: `crop.height ×
+/// crop.width` interleaved RGB, linear camera space, normalised black 0 and
+/// white 1 per photosite as the classical demosaic normalises.
+///
+/// `raw` must be hot-pixel repaired. `None` when `progress` stopped it.
+pub fn denoise(
+    raw: &RawImage,
+    noise: &NoiseModel,
+    net: &mut dyn TileNet,
+    progress: &mut dyn FnMut(usize, usize) -> bool,
+) -> Result<Option<Vec<f32>>, DenoiseError> {
+    if !eligible(raw) {
+        return Err(DenoiseError::Unsupported(format!(
+            "{:?} with {} samples per photosite",
+            raw.cfa_pattern, raw.samples_per_pixel
+        )));
+    }
+    let active = noise::active(raw);
+    let (h, w) = (active.h, active.w);
+    tile::run_tiled(
+        net,
+        h,
+        w,
+        raw.cfa_pattern,
+        &|y, x| active.at(y, x),
+        &|c, v| noise.sigma(c, v),
+        progress,
+    )
+}

core/dr-denoise/src/noise.rs

@@ -0,0 +1,396 @@
+//! TRACES: FR-DEV-3g
+//! How noisy each photosite is: the network is told, not left to guess
+//! (denoise.md §3.3).
+//!
+//! The model is `σ² = S·x + O + row² + col²` per photosite, `x` the signal
+//! normalised black-to-white the way the demosaic normalises it. Three
+//! sources, best first:
+//!
+//! 1. **A measured table** for the body ([`Source::Table`]) — the Canon EOS 6D
+//!    today, from the library's own frames.
+//! 2. **The DNG's `NoiseProfile`** ([`Source::DngProfile`]) — what Adobe's
+//!    converter measured for the body at that ISO.
+//! 3. **The frame itself** ([`Source::Measured`]) — read, row and column
+//!    noise from its masked border, which is a dark frame taken in the same
+//!    instant, and only the shot gain estimated, from the quietest flat
+//!    patches. Checked against the 6D's table on 130 frames: within ±10 % at
+//!    ISO 1000 and above, scattered below; the network loses under 0.3 dB for
+//!    a σ off by 15–20 %, and over-estimating costs half what
+//!    under-estimating does, so the estimate leans high.
+//!
+//! Row and column noise come from the masked border whenever the frame has
+//! one, whatever the source of the rest.
+
+use dr_decode::{CfaPattern, RawImage};
+use serde::Deserialize;
+
+/// Where a frame's noise figures came from, for develop to say.
+#[derive(Clone, Copy, Debug, PartialEq, Eq)]
+pub enum Source {
+    Table,
+    DngProfile,
+    Measured,
+}
+
+impl Source {
+    pub fn label(self) -> &'static str {
+        match self {
+            Source::Table => "measured for this camera",
+            Source::DngProfile => "from the DNG's noise profile",
+            Source::Measured => "estimated from this photograph",
+        }
+    }
+}
+
+/// Per-photosite noise in the frame's own normalisation (black 0, white 1).
+#[derive(Clone, Debug, PartialEq)]
+pub struct NoiseModel {
+    /// Shot gain per colour, R G B.
+    pub s: [f32; 3],
+    /// Read variance per colour, R G B.
+    pub o: [f32; 3],
+    /// Standard deviation shared by a whole row, and by a whole column.
+    pub row: f32,
+    pub col: f32,
+    pub source: Source,
+}
+
+impl NoiseModel {
+    /// σ for a photosite of colour `c` (0 R, 1 G, 2 B) reading `x`.
+    #[inline]
+    pub fn sigma(&self, c: usize, x: f32) -> f32 {
+        (self.s[c] * x.max(0.0) + self.o[c] + self.row * self.row + self.col * self.col).sqrt()
+    }
+
+    /// The same figures scaled for the Amount the spec describes (§3.3):
+    /// above 1 tells the network there is more noise than there is.
+    pub fn scaled(&self, amount: f32) -> NoiseModel {
+        let a2 = amount * amount;
+        NoiseModel {
+            s: self.s.map(|v| v * a2),
+            o: self.o.map(|v| v * a2),
+            row: self.row * amount,
+            col: self.col * amount,
+            source: self.source,
+        }
+    }
+}
+
+/// The frame's noise, from the best source it has.
+///
+/// `bytes` is the file (for a DNG's `NoiseProfile`), `iso` its EXIF ISO.
+/// `None` only for a frame with no masked border, no profile and no table
+/// that is also too dark or too busy to measure.
+pub fn for_frame(raw: &RawImage, bytes: &[u8], iso: Option<u32>) -> Option<NoiseModel> {
+    let dark = dark_border(raw);
+    let mut model = iso
+        .and_then(|iso| from_table(raw, iso))
+        .or_else(|| dr_decode::noise_profile(bytes).and_then(|p| from_dng_profile(raw, &p)))
+        .or_else(|| measured(raw, dark.as_ref()))?;
+    if let Some(d) = dark {
+        // The border saw this exposure's row and column noise directly.
+        if model.source != Source::Table {
+            model.row = d.row;
+            model.col = d.col;
+        }
+    }
+    Some(model)
+}
+
+#[derive(Deserialize)]
+struct Table {
+    make: String,
+    model: String,
+    rows: Vec<TableRow>,
+}
+
+#[derive(Deserialize)]
+struct TableRow {
+    iso: u32,
+    s_dn: [f32; 4],
+    o_dn: [f32; 4],
+    row_dn: f32,
+    col_dn: f32,
+}
+
+const TABLES: &[&str] = &[include_str!("../tables/canon-eos-6d.yaml")];
+
+/// The body's measured table at the nearest ISO it holds, converted from DN
+/// to this frame's normalisation.
+pub fn from_table(raw: &RawImage, iso: u32) -> Option<NoiseModel> {
+    let table = TABLES.iter().find_map(|t| {
+        let t: Table = serde_norway::from_str(t).ok()?;
+        (t.make.eq_ignore_ascii_case(&raw.make) && t.model.eq_ignore_ascii_case(&raw.model))
+            .then_some(t)
+    })?;
+    let row = table.rows.iter().min_by(|a, b| {
+        let d = |r: &TableRow| ((r.iso as f32).ln() - (iso as f32).ln()).abs();
+        d(a).total_cmp(&d(b))
+    })?;
+    let span = span(raw);
+    // RGGB positions → colours: the greens share.
+    let s = [row.s_dn[0], 0.5 * (row.s_dn[1] + row.s_dn[2]), row.s_dn[3]].map(|v| v / span);
+    let o =
+        [row.o_dn[0], 0.5 * (row.o_dn[1] + row.o_dn[2]), row.o_dn[3]].map(|v| v / (span * span));
+    Some(NoiseModel {
+        s,
+        o,
+        row: row.row_dn / span,
+        col: row.col_dn / span,
+        source: Source::Table,
+    })
+}
+
+/// A DNG's `NoiseProfile`: one pair for every plane, or one per colour plane
+/// (R, G, B for a Bayer DNG), already in the file's black-to-white units —
+/// which are the units `dr-decode` normalises by.
+pub fn from_dng_profile(raw: &RawImage, pairs: &[(f32, f32)]) -> Option<NoiseModel> {
+    if raw.cfa_pattern.is_xtrans() || raw.samples_per_pixel != 1 {
+        return None;
+    }
+    let (s, o) = match pairs {
+        [(s, o)] => ([*s; 3], [*o; 3]),
+        [r, g, b, ..] => ([r.0, g.0, b.0], [r.1, g.1, b.1]),
+        _ => return None,
+    };
+    Some(NoiseModel {
+        s,
+        o,
+        row: 0.0,
+        col: 0.0,
+        source: Source::DngProfile,
+    })
+}
+
+/// Read, row and column noise measured on the masked border, normalised.
+#[derive(Clone, Copy, Debug)]
+pub struct Dark {
+    pub read: f32,
+    pub row: f32,
+    pub col: f32,
+}
+
+/// The optically black photosites beside and above the active area.
+///
+/// Keeps well clear of the active area: on the 6D the dozen columns nearest
+/// it see light. Photosites over 8σ are the strip's own hot photosites — the
+/// same ones in every frame — and are left out, as the app's hot-pixel pass
+/// removes their kin before the network sees them.
+pub fn dark_border(raw: &RawImage) -> Option<Dark> {
+    let (x0, y0, w, h) = (
+        raw.crop.x as usize,
+        raw.crop.y as usize,
+        raw.crop.width as usize,
+        raw.crop.height as usize,
+    );
+    let stride = raw.width as usize;
+    let span = span(raw);
+    if x0 < 40 || raw.samples_per_pixel != 1 {
+        return None;
+    }
+    let cols = 4..x0 - 16;
+    let nc = cols.len() as f32;
+    // Residual after removing each row's mean and each column's mean.
+    let mut row_means = Vec::with_capacity(h);
+    let mut col_sum = vec![0.0f64; cols.len()];
+    for y in y0..y0 + h {
+        let line = &raw.data[y * stride..y * stride + x0];
+        let m = cols.clone().map(|x| line[x] as f32).sum::<f32>() / nc;
+        row_means.push(m);
+        for (k, x) in cols.clone().enumerate() {
+            col_sum[k] += (line[x] as f32 - m) as f64;
+        }
+    }
+    let col_mean: Vec<f32> = col_sum.iter().map(|s| (*s / h as f64) as f32).collect();
+    let resid = |y: usize, k: usize, x: usize| {
+        raw.data[y * stride + x] as f32 - row_means[y - y0] - col_mean[k]
+    };
+    let (mut s1, mut n) = (0.0f64, 0usize);
+    for y in y0..y0 + h {
+        for (k, x) in cols.clone().enumerate() {
+            s1 += (resid(y, k, x) as f64).powi(2);
+            n += 1;
+        }
+    }
+    let rough = (s1 / n as f64).sqrt() as f32;
+    let (mut s2, mut n2) = (0.0f64, 0usize);
+    for y in y0..y0 + h {
+        for (k, x) in cols.clone().enumerate() {
+            let r = resid(y, k, x);
+            if r.abs() < 8.0 * rough {
+                s2 += (r as f64).powi(2);
+                n2 += 1;
+            }
+        }
+    }
+    let read = (s2 / n2.max(1) as f64).sqrt() as f32;
+    let rm = row_means.iter().sum::<f32>() / h as f32;
+    let row_var = row_means.iter().map(|m| (m - rm).powi(2)).sum::<f32>() / h as f32;
+    let row = (row_var - read * read / nc).max(0.0).sqrt();
+
+    // Columns: the masked rows above the image span every column.
+    let col = if y0 >= 24 {
+        let rows = 4..y0 - 12;
+        let nr = rows.len() as f32;
+        let means: Vec<f32> = (x0..x0 + w)
+            .map(|x| {
+                rows.clone()
+                    .map(|y| raw.data[y * stride + x] as f32)
+                    .sum::<f32>()
+                    / nr
+            })
+            .collect();
+        let mm = means.iter().sum::<f32>() / means.len() as f32;
+        let var = means.iter().map(|m| (m - mm).powi(2)).sum::<f32>() / means.len() as f32;
+        (var - read * read / nr).max(0.0).sqrt()
+    } else {
+        0.0
+    };
+    Some(Dark {
+        read: read / span,
+        row: row / span,
+        col: col / span,
+    })
+}
+
+/// The quietest-third bias of the patch variance, and the residual bias the
+/// estimate showed against the 6D's table (0.91 at the median), in one: the
+/// estimate is divided by this.
+const QUIET_FACTOR: f32 = 0.85 * 0.91;
+
+/// The frame's own noise: read noise from the border (or, lacking one, the
+/// floor of the quietest patches), shot gain from flat patches of one green
+/// plane, the same for every colour, as a sensor's gain is.
+pub fn measured(raw: &RawImage, dark: Option<&Dark>) -> Option<NoiseModel> {
+    if raw.cfa_pattern.is_xtrans() || raw.samples_per_pixel != 1 {
+        return None;
+    }
+    let m = active(raw);
+    let (h, w) = (m.h, m.w);
+    // One green plane at a two-photosite pitch.
+    let (gy, gx) = green_offset(raw.cfa_pattern)?;
+    let ph = (h - gy) / 2;
+    let pw = (w - gx) / 2;
+    let g = |y: usize, x: usize| m.at(gy + 2 * y, gx + 2 * x);
+    const B: usize = 8;
+    let mut patches: Vec<(f32, f32)> = Vec::new(); // (level, variance)
+    for by in 0..ph / B {
+        for bx in 0..(pw - 2) / B {
+            let (mut s, mut s2, mut lv) = (0.0f32, 0.0f32, 0.0f32);
+            for y in by * B..by * B + B {
+                for x in bx * B..bx * B + B {
+                    // Second difference: cancels any gradient; var = 6σ².
+                    let d = g(y, x + 2) - 2.0 * g(y, x + 1) + g(y, x);
+                    s += d;
+                    s2 += d * d;
+                    lv += g(y, x + 1);
+                }
+            }
+            let n = (B * B) as f32;
+            let var = (s2 / n - (s / n).powi(2)) / 6.0;
+            patches.push((lv / n, var));
+        }
+    }
+    let floor = dark.map(|d| d.read);
+    let lo = 4.0 * floor.unwrap_or(0.002);
+    patches.retain(|(l, _)| *l > lo && *l < 0.7);
+    if patches.len() < 500 {
+        return None;
+    }
+    patches.sort_by(|a, b| a.0.total_cmp(&b.0));
+    let bins = 12;
+    let per = patches.len() / bins;
+    let mut ests = Vec::new();
+    let mut floors = Vec::new();
+    for b in 0..bins {
+        let mut bin: Vec<(f32, f32)> = patches[b * per..(b + 1) * per].to_vec();
+        if bin.len() < 60 {
+            continue;
+        }
+        bin.sort_by(|a, b| a.1.total_cmp(&b.1));
+        let quiet = &bin[..bin.len() / 3];
+        let read2 = floor.map(|r| r * r);
+        let mut e: Vec<f32> = quiet
+            .iter()
+            .map(|(l, v)| (v / QUIET_FACTOR - read2.unwrap_or(0.0)) / l)
+            .collect();
+        e.sort_by(f32::total_cmp);
+        ests.push(e[e.len() / 2]);
+        floors.push(quiet[quiet.len() / 2]);
+    }
+    ests.sort_by(f32::total_cmp);
+    let s = *ests.get(ests.len() / 2)?;
+    if !(s.is_finite() && s > 0.0) {
+        return None;
+    }
+    // No border: the read variance is what the darkest bin leaves unexplained.
+    let read2 = match floor {
+        Some(r) => r * r,
+        None => {
+            let (l, v) = floors.first().copied()?;
+            (v / QUIET_FACTOR - s * l).max(1e-9)
+        }
+    };
+    Some(NoiseModel {
+        s: [s; 3],
+        o: [read2; 3],
+        row: dark.map_or(0.0, |d| d.row),
+        col: dark.map_or(0.0, |d| d.col),
+        source: Source::Measured,
+    })
+}
+
+/// Black-to-white range of the frame, as the demosaic normalises it.
+pub(crate) fn span(raw: &RawImage) -> f32 {
+    let black = raw.black_level.iter().map(|&b| b as f32).sum::<f32>() / 4.0;
+    (raw.white_level as f32 - black).max(1.0)
+}
+
+/// Where a green photosite sits in the pattern's 2×2 cell, (dy, dx).
+fn green_offset(p: CfaPattern) -> Option<(usize, usize)> {
+    match p {
+        CfaPattern::Rggb | CfaPattern::Bggr => Some((0, 1)),
+        CfaPattern::Grbg | CfaPattern::Gbrg => Some((0, 0)),
+        _ => None,
+    }
+}
+
+/// The active area, normalised, read lazily.
+pub(crate) struct Active<'a> {
+    raw: &'a RawImage,
+    black: [f32; 4],
+    inv: [f32; 4],
+    pub h: usize,
+    pub w: usize,
+}
+
+impl Active<'_> {
+    /// Photosite (y, x) of the active area, black 0, white 1.
+    #[inline]
+    pub fn at(&self, y: usize, x: usize) -> f32 {
+        let c = (y & 1) * 2 + (x & 1);
+        let v = self.raw.data[(self.raw.crop.y as usize + y) * self.raw.width as usize
+            + self.raw.crop.x as usize
+            + x];
+        (v as f32 - self.black[c]) * self.inv[c]
+    }
+}
+
+/// Black levels per position of the crop's 2×2 cell, as the demosaic reads
+/// them: one reported level is broadcast.
+pub(crate) fn active(raw: &RawImage) -> Active<'_> {
+    let b = raw.black_level;
+    let black = if b[1] == 0 && b[2] == 0 && b[3] == 0 {
+        [b[0] as f32; 4]
+    } else {
+        b.map(|v| v as f32)
+    };
+    let inv = black.map(|bl| 1.0 / (raw.white_level as f32 - bl).max(1.0));
+    Active {
+        raw,
+        black,
+        inv,
+        h: raw.crop.height as usize,
+        w: raw.crop.width as usize,
+    }
+}

core/dr-denoise/src/onnx.rs

@@ -0,0 +1,66 @@
+//! TRACES: FR-DEV-3g
+//! The denoise network under the inference engine.
+//!
+//! The shipped export takes `mosaic` and `sigma`, `1×1×1408×1408`, and
+//! returns `rgb`, `1×3×1408×1408` (darkroom-denoise `denoise/export.py`,
+//! fixed shape because every model the engine runs is). The engine picks the
+//! rung: fp16 on TensorRT and MIGraphX, which measured 0.00 dB from f32; f32
+//! on CUDA and the CPU; never the Hexagon, where int8 lost 6–9 dB.
+
+use crate::tile::TileNet;
+use crate::DenoiseError;
+use dr_inference_engine::{Model, Role};
+
+/// The edge of the tile the shipped export takes.
+pub const TILE: usize = 1408;
+
+pub struct OnnxNet {
+    model: Model,
+    tile: usize,
+}
+
+impl OnnxNet {
+    pub fn from_path(path: &std::path::Path) -> Result<Self, DenoiseError> {
+        let (path, form) = dr_inference_engine::resolve_model(Role::Denoiser, path);
+        let bytes = std::fs::read(&path)?;
+        Ok(OnnxNet {
+            model: dr_inference_engine::open(Role::Denoiser, form, &bytes)?,
+            tile: TILE,
+        })
+    }
+
+    /// Where it runs, for a status line.
+    pub fn rung(&self) -> Result<dr_inference_engine::Rung, DenoiseError> {
+        Ok(self.model.acquire()?.rung())
+    }
+}
+
+impl TileNet for OnnxNet {
+    fn tile(&self) -> usize {
+        self.tile
+    }
+
+    fn run(&mut self, mosaic: &[f32], sigma: &[f32]) -> Result<Vec<f32>, DenoiseError> {
+        let n = self.tile;
+        let shape = ndarray::IxDyn(&[1, 1, n, n]);
+        let m = ort::value::Tensor::from_array(
+            ndarray::Array::from_shape_vec(shape.clone(), mosaic.to_vec())
+                .map_err(|e| DenoiseError::Model(e.to_string()))?,
+        )?;
+        let s = ort::value::Tensor::from_array(
+            ndarray::Array::from_shape_vec(shape, sigma.to_vec())
+                .map_err(|e| DenoiseError::Model(e.to_string()))?,
+        )?;
+        let acquired = self.model.acquire()?;
+        let mut session = acquired.lock();
+        let outputs = session.run(ort::inputs!["mosaic" => m, "sigma" => s])?;
+        let (shape, data) = outputs[0].try_extract_tensor::<f32>()?;
+        let dims: Vec<i64> = shape.iter().copied().collect();
+        if dims != [1, 3, n as i64, n as i64] {
+            return Err(DenoiseError::Model(format!(
+                "output is {dims:?}, expected [1, 3, {n}, {n}]"
+            )));
+        }
+        Ok(data.to_vec())
+    }
+}

core/dr-denoise/src/tile.rs

@@ -0,0 +1,295 @@
+//! TRACES: FR-DEV-3g
+//! A whole frame through a fixed-shape network, exactly (denoise.md §3.4).
+//!
+//! The network sees `TILE_IN`² photosites and its output is exact in the
+//! central `TILE_IN − 2·HALO`: the halo is wider than its receptive field
+//! (185 photosites, counted from the layers), so a tile's centre equals the
+//! whole frame's at the same place. The frame is extended by reflection
+//! about its edge photosites, which keeps every photosite's CFA colour, so
+//! edge tiles see real context too.
+//!
+//! **Phase.** The network was trained on RGGB. A frame whose pattern starts
+//! on another colour is read from one photosite up and/or left — the
+//! reflection supplies that row or column — so its top-left is red, and the
+//! output is read back from the same offset. Nothing is cropped.
+
+use dr_decode::CfaPattern;
+
+/// Photosites of context beyond a tile's kept centre, on every side.
+pub const HALO: usize = 192;
+
+/// A fixed-shape network: `mosaic` and `sigma`, `n×n` RGGB, in; `3×n×n`
+/// planar linear camera RGB out.
+pub trait TileNet {
+    /// The edge `n` of the square tile the network takes.
+    fn tile(&self) -> usize;
+    fn run(&mut self, mosaic: &[f32], sigma: &[f32]) -> Result<Vec<f32>, crate::DenoiseError>;
+}
+
+/// Index into `0..n` by reflection about the end photosites, any distance
+/// out: …2 1 [0 1 2 … n−1] n−2 n−3…, period `2(n−1)`. Parity is kept, which
+/// is what keeps a CFA colour.
+#[inline]
+pub fn reflect(i: isize, n: usize) -> usize {
+    if n == 1 {
+        return 0;
+    }
+    let p = 2 * (n as isize - 1);
+    let m = i.rem_euclid(p);
+    (if m < n as isize { m } else { p - m }) as usize
+}
+
+/// How far up and left to start reading so the first photosite is red.
+pub fn rggb_offset(p: CfaPattern) -> Option<(usize, usize)> {
+    match p {
+        CfaPattern::Rggb => Some((0, 0)),
+        CfaPattern::Grbg => Some((0, 1)),
+        CfaPattern::Gbrg => Some((1, 0)),
+        CfaPattern::Bggr => Some((1, 1)),
+        _ => None,
+    }
+}
+
+/// Run `net` over an `h×w` mosaic given by `at(y, x)`, with σ from
+/// `sigma(colour, value)`, and return `h×w` interleaved RGB.
+///
+/// `progress(done, total)` is called after each tile and stops the run by
+/// returning `false`, in which case the result is `Ok(None)`.
+#[allow(clippy::too_many_arguments)]
+pub fn run_tiled(
+    net: &mut dyn TileNet,
+    h: usize,
+    w: usize,
+    pattern: CfaPattern,
+    at: &dyn Fn(usize, usize) -> f32,
+    sigma: &dyn Fn(usize, f32) -> f32,
+    progress: &mut dyn FnMut(usize, usize) -> bool,
+) -> Result<Option<Vec<f32>>, crate::DenoiseError> {
+    let (dy, dx) = rggb_offset(pattern).ok_or_else(|| {
+        crate::DenoiseError::Unsupported(format!("{pattern:?} is not a Bayer pattern"))
+    })?;
+    let n = net.tile();
+    if n <= 2 * HALO || !(n - 2 * HALO).is_multiple_of(2) {
+        return Err(crate::DenoiseError::Model(format!(
+            "tile {n} leaves no even centre past a {HALO} halo"
+        )));
+    }
+    let core = n - 2 * HALO;
+    // In unified coordinates the frame spans u ∈ [dy, dy + h), v ∈ [dx, dx + w).
+    let (uh, uw) = (h + dy, w + dx);
+    let (ty, tx) = (uh.div_ceil(core), uw.div_ceil(core));
+    let total = ty * tx;
+    let mut out = vec![0.0f32; h * w * 3];
+    let mut mos = vec![0.0f32; n * n];
+    let mut sig = vec![0.0f32; n * n];
+    // RGGB colour of unified position (u, v).
+    let colour = |u: usize, v: usize| [[0, 1], [1, 2]][u & 1][v & 1];
+    for (k, (i, j)) in (0..ty)
+        .flat_map(|i| (0..tx).map(move |j| (i, j)))
+        .enumerate()
+    {
+        let (u0, v0) = (i * core, j * core);
+        for r in 0..n {
+            // Unified row u = u0 + r − HALO; frame row y = u − dy, reflected.
+            let u = u0 as isize + r as isize - HALO as isize;
+            let y = reflect(u - dy as isize, h);
+            for c in 0..n {
+                let v = v0 as isize + c as isize - HALO as isize;
+                let x = reflect(v - dx as isize, w);
+                let val = at(y, x);
+                mos[r * n + c] = val;
+                sig[r * n + c] = sigma(colour(r, c), val);
+            }
+        }
+        let rgb = net.run(&mos, &sig)?;
+        if rgb.len() != 3 * n * n {
+            return Err(crate::DenoiseError::Model(format!(
+                "network returned {} values for a {n}² tile",
+                rgb.len()
+            )));
+        }
+        for r in HALO..HALO + core {
+            let u = u0 + r - HALO;
+            if u < dy || u >= uh {
+                continue;
+            }
+            let y = u - dy;
+            for c in HALO..HALO + core {
+                let v = v0 + c - HALO;
+                if v < dx || v >= uw {
+                    continue;
+                }
+                let x = v - dx;
+                let o = (y * w + x) * 3;
+                for ch in 0..3 {
+                    out[o + ch] = rgb[ch * n * n + r * n + c];
+                }
+            }
+        }
+        if !progress(k + 1, total) {
+            return Ok(None);
+        }
+    }
+    Ok(Some(out))
+}
+
+#[cfg(test)]
+mod tests {
+    use super::*;
+
+    #[test]
+    fn reflection_keeps_parity_any_distance_out() {
+        let n = 7;
+        for i in -40isize..40 {
+            let r = reflect(i, n);
+            assert!(r < n);
+            assert_eq!(
+                r % 2,
+                i.rem_euclid(2) as usize,
+                "index {i} reflected to {r}"
+            );
+        }
+        assert_eq!(reflect(-1, n), 1);
+        assert_eq!(reflect(7, n), 5);
+    }
+
+    /// A stand-in network with a known, finite reach: each output photosite
+    /// is its 2×2 quad's (R, mean G, B), averaged over the quads within
+    /// `reach` quads. Purely a function of the tile, like the real one.
+    struct BoxNet {
+        n: usize,
+        reach: usize,
+    }
+
+    impl TileNet for BoxNet {
+        fn tile(&self) -> usize {
+            self.n
+        }
+        fn run(&mut self, m: &[f32], _s: &[f32]) -> Result<Vec<f32>, crate::DenoiseError> {
+            let n = self.n;
+            let q = n / 2;
+            let quad = |qy: usize, qx: usize| {
+                let (y, x) = (2 * qy, 2 * qx);
+                [
+                    m[y * n + x],
+                    0.5 * (m[y * n + x + 1] + m[(y + 1) * n + x]),
+                    m[(y + 1) * n + x + 1],
+                ]
+            };
+            let mut out = vec![0.0; 3 * n * n];
+            for qy in 0..q {
+                for qx in 0..q {
+                    let mut acc = [0.0f32; 3];
+                    let mut cnt = 0.0;
+                    for a in qy.saturating_sub(self.reach)..(qy + self.reach + 1).min(q) {
+                        for b in qx.saturating_sub(self.reach)..(qx + self.reach + 1).min(q) {
+                            let v = quad(a, b);
+                            for c in 0..3 {
+                                acc[c] += v[c];
+                            }
+                            cnt += 1.0;
+                        }
+                    }
+                    for (dy, dx) in [(0, 0), (0, 1), (1, 0), (1, 1)] {
+                        for c in 0..3 {
+                            out[c * n * n + (2 * qy + dy) * n + 2 * qx + dx] = acc[c] / cnt;
+                        }
+                    }
+                }
+            }
+            Ok(out)
+        }
+    }
+
+    /// The mosaic of a smooth colour field in `pattern`, read at (y, x).
+    fn field(pattern: CfaPattern) -> impl Fn(usize, usize) -> f32 {
+        move |y, x| {
+            let rgb = [0.2 + 0.0004 * x as f32, 0.5, 0.1 + 0.0003 * y as f32];
+            rgb[pattern.colour_at(x as u32, y as u32) as usize]
+        }
+    }
+
+    #[test]
+    fn every_bayer_phase_comes_back_as_its_own_colours() {
+        // A frame of each pattern, its colours known: the network must see
+        // red where the frame's red photosites are, whatever the phase.
+        for p in [
+            CfaPattern::Rggb,
+            CfaPattern::Grbg,
+            CfaPattern::Gbrg,
+            CfaPattern::Bggr,
+        ] {
+            let (h, w) = (300, 410);
+            let at = field(p);
+            let mut net = BoxNet {
+                n: 2 * HALO + 64,
+                reach: 0,
+            };
+            let out = run_tiled(&mut net, h, w, p, &at, &|_, _| 0.01, &mut |_, _| true)
+                .unwrap()
+                .unwrap();
+            for (y, x) in [(10, 10), (150, 201), (299, 409), (0, 0), (77, 333)] {
+                let o = &out[(y * w + x) * 3..(y * w + x) * 3 + 3];
+                let want = [0.2 + 0.0004 * x as f32, 0.5, 0.1 + 0.0003 * y as f32];
+                for c in 0..3 {
+                    // Within the quad the binned value is at most a photosite away.
+                    assert!(
+                        (o[c] - want[c]).abs() < 0.0012,
+                        "{p:?} at ({y},{x}) channel {c}: {} vs {}",
+                        o[c],
+                        want[c]
+                    );
+                }
+            }
+        }
+    }
+
+    #[test]
+    fn tiles_reproduce_one_pass_over_the_reflected_frame() {
+        // A network whose reach is inside the halo gives the same answer
+        // tiled small as in one tile covering everything.
+        let (h, w) = (230, 170);
+        for p in [CfaPattern::Rggb, CfaPattern::Bggr] {
+            let at = |y: usize, x: usize| ((y * 7919 + x * 104729) % 1000) as f32 / 1000.0;
+            let mut small = BoxNet {
+                n: 2 * HALO + 32,
+                reach: 20,
+            };
+            let mut big = BoxNet {
+                n: 2 * HALO + 256,
+                reach: 20,
+            };
+            let a = run_tiled(&mut small, h, w, p, &at, &|_, _| 0.0, &mut |_, _| true)
+                .unwrap()
+                .unwrap();
+            let b = run_tiled(&mut big, h, w, p, &at, &|_, _| 0.0, &mut |_, _| true)
+                .unwrap()
+                .unwrap();
+            let worst = a
+                .iter()
+                .zip(&b)
+                .map(|(x, y)| (x - y).abs())
+                .fold(0.0f32, f32::max);
+            assert!(worst < 1e-5, "{p:?}: tiled and whole differ by {worst}");
+        }
+    }
+
+    #[test]
+    fn a_cancelled_run_returns_nothing() {
+        let mut net = BoxNet {
+            n: 2 * HALO + 32,
+            reach: 0,
+        };
+        let r = run_tiled(
+            &mut net,
+            100,
+            100,
+            CfaPattern::Rggb,
+            &|_, _| 0.5,
+            &|_, _| 0.0,
+            &mut |done, _| done < 2,
+        )
+        .unwrap();
+        assert!(r.is_none());
+    }
+}

core/dr-denoise/tables/canon-eos-6d.yaml

@@ -0,0 +1,36 @@
+# Canon EOS 6D noise, measured from the library's own frames (denoise.md §5).
+# Shot gain S and read variance O per RGGB position from Adobe's NoiseProfile in
+# converted DNGs, in DN at the ISO's own white level; read noise checked against
+# the masked border (within 2-3 %); row and column noise from the masked border.
+# ISO 50 and 100 are extrapolated (S proportional to ISO). Generated by
+# darkroom-denoise tools/profile.py; regenerate there, never edit by hand.
+make: Canon
+model: EOS 6D
+black: 2048
+rows:
+  - {iso: 50, white: 15000, s_dn: [0.0854021, 0.085467, 0.085467, 0.0839724], o_dn: [38.2741, 38.6675, 38.6675, 38.9649], row_dn: 0.3423, col_dn: 0.505}
+  - {iso: 100, white: 15000, s_dn: [0.170804, 0.170934, 0.170934, 0.167945], o_dn: [38.339, 38.7332, 38.7332, 39.031], row_dn: 0.3423, col_dn: 0.505}
+  - {iso: 125, white: 15035, s_dn: [0.228108, 0.230361, 0.230361, 0.228345], o_dn: [36.9455, 37.8995, 37.8995, 38.2358], row_dn: 0.3423, col_dn: 0.505}
+  - {iso: 160, white: 12373, s_dn: [0.289653, 0.294915, 0.294915, 0.286887], o_dn: [15.3717, 16.1346, 16.1346, 16.0413], row_dn: 0.212, col_dn: 0.07151}
+  - {iso: 200, white: 15035, s_dn: [0.370969, 0.369922, 0.369922, 0.361443], o_dn: [24.2761, 24.0847, 24.0847, 24.2414], row_dn: 0.2692, col_dn: 0}
+  - {iso: 250, white: 15035, s_dn: [0.461889, 0.457975, 0.457975, 0.449318], o_dn: [38.0975, 37.5041, 37.5041, 37.7424], row_dn: 0.3345, col_dn: 0.4786}
+  - {iso: 320, white: 12323, s_dn: [0.590765, 0.59755, 0.59755, 0.576843], o_dn: [18.5426, 18.9163, 18.9163, 19.1202], row_dn: 0.3158, col_dn: 0.5174}
+  - {iso: 400, white: 15035, s_dn: [0.753591, 0.740586, 0.740586, 0.729874], o_dn: [29.3028, 29.8496, 29.8496, 29.7961], row_dn: 0.4378, col_dn: 0.2691}
+  - {iso: 500, white: 15035, s_dn: [0.937458, 0.920836, 0.920836, 0.899293], o_dn: [45.2196, 46.3954, 46.3954, 46.1012], row_dn: 0.5473, col_dn: 0.4328}
+  - {iso: 640, white: 12323, s_dn: [1.12726, 1.13527, 1.13527, 1.10159], o_dn: [24.9951, 25.1029, 25.1029, 25.7183], row_dn: 0.316, col_dn: 0.4544}
+  - {iso: 800, white: 15035, s_dn: [1.44048, 1.42299, 1.42299, 1.40795], o_dn: [38.7891, 39.302, 39.302, 40.079], row_dn: 0.3877, col_dn: 0.2132}
+  - {iso: 1000, white: 15000, s_dn: [1.77595, 1.75662, 1.75662, 1.74584], o_dn: [63.9499, 64.4203, 64.4203, 65.0739], row_dn: 0.4593, col_dn: 0.3307}
+  - {iso: 1250, white: 12346, s_dn: [2.18211, 2.18313, 2.18313, 2.11979], o_dn: [41.6124, 42.9483, 42.9483, 43.2075], row_dn: 0.3979, col_dn: 0.4496}
+  - {iso: 1600, white: 15035, s_dn: [2.75544, 2.74633, 2.74633, 2.69951], o_dn: [66.3905, 66.4104, 66.4104, 67.253], row_dn: 0.4944, col_dn: 0.4593}
+  - {iso: 2000, white: 15035, s_dn: [3.42754, 3.40445, 3.40445, 3.36808], o_dn: [104.349, 103.648, 103.648, 106.404], row_dn: 0.6094, col_dn: 0.3602}
+  - {iso: 2500, white: 12330, s_dn: [4.17112, 4.17551, 4.17551, 4.17175], o_dn: [94.4289, 91.8508, 91.8508, 96.3598], row_dn: 0.5671, col_dn: 0}
+  - {iso: 3200, white: 15035, s_dn: [5.30088, 5.25742, 5.25742, 5.21782], o_dn: [147.421, 147.302, 147.302, 147.01], row_dn: 0.748, col_dn: 0.8611}
+  - {iso: 4000, white: 15035, s_dn: [6.62037, 6.59922, 6.59922, 6.60871], o_dn: [224.765, 232.408, 232.408, 231.419], row_dn: 0.9335, col_dn: 1.125}
+  - {iso: 5000, white: 12323, s_dn: [8.49542, 8.48265, 8.48265, 8.41176], o_dn: [232.672, 233.922, 233.922, 256.059], row_dn: 1.085, col_dn: 1.852}
+  - {iso: 6400, white: 15035, s_dn: [10.6956, 10.7417, 10.7417, 10.6503], o_dn: [360.311, 368.198, 368.198, 362.848], row_dn: 1.326, col_dn: 2.277}
+  - {iso: 8000, white: 15035, s_dn: [13.1307, 13.3864, 13.3864, 13.147], o_dn: [615.02, 566.666, 566.666, 611.738], row_dn: 1.768, col_dn: 3.141}
+  - {iso: 10000, white: 12365, s_dn: [16.5338, 16.7603, 16.7603, 16.4739], o_dn: [914.064, 904.583, 904.583, 938.024], row_dn: 2.214, col_dn: 3.605}
+  - {iso: 12800, white: 15000, s_dn: [18.4717, 20.9315, 20.9315, 19.3821], o_dn: [1431.85, 1432.33, 1432.33, 1477.82], row_dn: 2.568, col_dn: 4.661}
+  - {iso: 16000, white: 15000, s_dn: [20.527, 26.0841, 26.0841, 21.8866], o_dn: [2203.77, 2357.78, 2357.78, 2193.1], row_dn: 3.521, col_dn: 5.805}
+  - {iso: 20000, white: 13000, s_dn: [25.1517, 32.5307, 32.5307, 26.2303], o_dn: [3490.34, 3647.93, 3647.93, 3423.33], row_dn: 4.336, col_dn: 7.143}
+  - {iso: 25600, white: 15000, s_dn: [22.8743, 40.4641, 40.4641, 23.5537], o_dn: [5184.57, 5690.43, 5690.43, 5286.07], row_dn: 5.682, col_dn: 9.193}

models/LICENCE.md

@@ -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).