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.
2026-10-03 11:15:50 -04:00
parent 20b7bd7663
commit d8304d7c82
12 changed files with 1072 additions and 1 deletions
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+# 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}