{"id":3331,"date":"2025-08-27T14:38:25","date_gmt":"2025-08-27T12:38:25","guid":{"rendered":"https:\/\/perceive-horizon.eu\/?page_id=3331"},"modified":"2025-09-24T17:19:25","modified_gmt":"2025-09-24T15:19:25","slug":"tool-degreening","status":"publish","type":"page","link":"https:\/\/perceive-horizon.eu\/index.php\/results\/perceive-tools\/tool-degreening\/","title":{"rendered":"Tool Degreening"},"content":{"rendered":"

Degreening Tool<\/h2>\n
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Autochromes are one of the first successful colour photography processes and are therefore significant to the history of photography. Autochromes are incredibly fragile and light sensitive, and when damaged, this damage cannot be easily reversed. As part of PERCEIVE, we are training AI models to restore images of autochrome plates exhibiting greening defects. To do so, we simulate these defects (described as synthetic defects) since we lack reference restored images.<\/p>\n<\/div>\n

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Synthetic generation of greening defects<\/h6>\n

To train our restoration models we do not have reference or ground-truth images of their restored version. Hence, we generate these defects synthetically utilizing image processing. We first analyzed genuine greening defects and found that they typically appear either as small, point-like stains or as broader, leakage-style areas, each exhibiting multi-ringed green halos with subtle orange borders. We then reproduce these effects synthetically and apply them on digitized autochromes. The application of the type of greening defect is done randomly selecting whether an image will receive only spot defects, only large area defects, or both. For each defect, we draw an irregular oval (to mimic natural liquid spread), overlay concentric rings with slightly different green hues (and a faint orange outer ring), and apply a light Gaussian blur to soften the edges. The result is a large, diverse set of images bearing realistic-looking green marks that we can use to effectively train our restoration networks.<\/p>\n