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After digital cleaning: visualization of the dirt layer

机译：数字清洁后：可视化污垢层

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摘要

Completely non-invasive digital cleaning of Fernando Amorsolo's 1948 oil on canvas, Malacahang 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：费尔南多·阿莫索洛（Fernando Amorsolo）1948年在河边的马拉卡hang（Malacahang）上的布面油画的完全无创数字清洁是使用训练有素的神经网络实现的。数字清洁过程为数字清洁的绘画带来了更加鲜艳的色彩和更高的亮度。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，原始图像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。对于第一个可视化，矢量差被投影在中性色上并针对整个图像进行渲染。第二次可视化将颜色变化呈现为可叠加在白色图像或经过数字清洁的图像上的半透明污垢层。对于第三次可视化，我们将颜色变化建模为污垢层，该污垢层充当绘画图像上的滤镜。由此产生的颜色变化和污垢层可视化效果与实际感知的颜色变化一致，并且可以为由于曝光而导致的绘画颜色变化过程提供有价值的见解。

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