Soot damage is one of the typical types of deterioration seen in Dunhuang murals, though it is a somewhat a typical research topic when compared with other types of deterioration. Soot damage is particularly damaging to the color of murals and cannot be ameliorated by conventional physical restoration methods. Focusing on the soot damaged murals in Mogao cave156, and by conducting two simulation experiments of soot damage from the perspective of digital restoration and physicochemical analysis, this paper explores the pattern of changes in paint pigments under the influence of soot and collects relevant data on color change. The researchers then pose a digital restoration method for soot damaged murals based on machine learning that is applicable to the restoration of similarly colored murals.
