Colorizing genuine archival photographs: global palette + targeted scribbles
CNN colorization networks are trained on colour photos converted to grayscale. But real archival photographs (silver halide) don't share the same spectral sensitivities — the network fails on flags, monuments, well-known objects. I improve the output by combining two types of hints: an automated palette + manual scribbles where it matters.
When the dataset betrays the network
Spectral-sensitivity mismatch: training datasets (ImageNet, Places…) are digital RGB photos converted to grayscale via a weighted average of channels. Genuine silver-halide B&W photos react differently to light: silver halide ≠ Y'CbCr luminance. Grey levels do not map to the same colours.
Practical consequence: for a real archival photo, the predicted colours can be plausible but wrong. A tricolour flag painted uniformly. A monument painted in the wrong stone. A military uniform recognised in the wrong army.
Building on MCNA 2020: my previous paper proposed a global palette as a hint. Good intuition, but not enough alone for edge cases (unique, identifiable, error-sensitive objects).
Two hints, one colorizer
Global palette + targeted scribbles
The idea: combine two complementary types of hints. The global palette covers most of the frame. Manual scribbles are only used on residual objects where the palette is not enough.
- Automated global palette (inherited from MCNA 2020): a salient palette computed per scene. Good for movie colorization, automatable, precise enough for most scenes.
- Targeted manual scribbles: for regions where the global palette is not enough (flags, monuments, unique objects), the user drops a few localised colour patches — a few pixels are enough.
- No architecture change: hints (palette + scribbles) are injected as additional inputs to the pretrained network. Plug-in on existing colorizers.
- Usage strategy: 80% of colorization is handled by the palette, scribbles are only needed on about 15% of the surface. The ratio shifts with scene complexity.
What this method enables
Visibly better quality
On archival photos vs colorizer alone: sky, foliage, persons stay correct, AND flags, monuments, uniforms — which used to fail — now come out right.
Controlled interaction cost
The palette handles most of the frame automatically; scribbles are only needed on unique objects. Minutes of scribbling per scene, instead of hours of pixel-by-pixel work.
Open-source compatible
Tested on the most-cited open-source colorizers (Zhang/Isola/Efros, Iizuka/Simo-Serra/Ishikawa). No proprietary model required.
Tailored to silver-halide imagery
The pipeline explicitly addresses the domain shift between converted digital photos and genuine silver-halide B&W — a gap often ignored in the literature.
What this publication brings
- Clear identification of the domain shift between training datasets (RGB → grayscale) and archival photos (silver halide). A blind spot in the colorization literature, where grayscale is implicitly assumed universal — which is false for archives.
- Hybrid palette + scribbles methodology: a better trade-off between automation (palette) and targeted quality (scribbles). Not a binary choice between the two.
- Practical pipeline for restoring historical photos without retraining a dedicated colorizer — usable by restorers with an off-the-shelf open-source colorizer.
- Culmination of the series: EUVIP 2018 (NIR face recognition), EUVIP 2019 (handwriting anomaly detection), MCNA 2020 (colorization palette), IPAS 2020 (palette + scribbles). One thread runs through all four: guiding deep networks with structured hints.
Venue & context
- Published at IEEE IPAS 2020 (International Conference on Image Processing, Applications and Systems).
- Work carried out during my research master's at MIA Laboratory · University of La Rochelle.
- Keywords: CNN, colorization, archival photography, interactive colorization, domain shift.