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After digital cleaning: visualization of the dirt layer

机译：数字清洁后：污垢层的可视化

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Completely non-invasive digital cleaning of Fernando Amorsolo's 1948 oil on canvas, Malacanang by the River, is implemented using a trained neural network. The digital cleaning process results to more vivid colors and a higher luminosity for the digitally-cleaned painting. We propose three methods for visualizing the color change that occurred to a painting image after digital cleaning. For the first two visualizations, the color change between original and digitally-cleaned image is computed as a vector difference in RGB space. For the first visualization, the vector difference is projected on a neutral color and rendered for the whole image. The second visualization renders the color change as a translucent dirt layer that can be superimposed on a white image or on the digitally-cleaned image. For the third visualization, we model the color change as a dirt layer that acts as a filter on the painting image. The resulting color change and dirt layer visualizations are consistent with the actual perceived color change and could offer valuable insights to a painting's color changing process due to exposure.
机译：Fernando Amorsolo 1948年在河上的Fornando Amorsolo的1948年油的无侵入性数字清洁，通过培训的神经网络实施了Malacanang的Malacanang。数字清洁过程导致更鲜艳的色彩和更高的数字清洁绘画的亮度。我们提出了三种方法来可视化数字清洁后绘画图像发生的颜色变化。对于前两个可视化，原始和数字清洁图像之间的颜色变化被计算为RGB空间中的矢量差异。对于第一可视化，将载体差突出在中性颜色上并为整个图像呈现。第二可视化使颜色变化为半透明污垢层，其可以叠加在白色图像或数字清洁的图像上。对于第三个可视化，我们将颜色变化为作为绘画图像上的滤波器的污垢层模型。由此产生的颜色变化和污垢层可视化与实际的感知颜色变化一致，并且可以为曝光引起的绘画颜色变化过程提供有价值的见解。

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