Image illumination correction has been a long standing topic for research in the Computer Vision problem. However, all previous literature on this topic has either been statistical in nature in the sense that a specified algorithm has been developed for approaching a particular case of illumination normalization, or involves extremely complex deep learning methods for illumination correction of either one of over illuminated or under illuminated images. We present here a very simple deep learning based image illumination correction architecture which works on color images of paintings irrespective of whether they are under or over illuminated. We have tested the results using a synthetic database as well as on real world painting images of diverse nature.

图像照明校正一直是计算机视觉问题研究的长期课题。但是，有关该主题的所有先前文献在某种意义上都是统计学性质的，即已开发出一种特定的算法来解决照明标准化的特定情况，或者涉及极其复杂的深度学习方法，用于对过照明或过照明中的一种进行照明校正。在照明的图像下。我们在这里提出了一种非常简单的基于深度学习的图像照明校正体系结构，该体系结构适用于绘画的彩色图像，而不管它们是处于照明不足还是过度照明的状态。我们使用合成数据库以及各种性质的真实世界绘画图像对结果进行了测试。