Cognitive Computing for Understanding and Restoring Color in Renaissance Art

In this article, for the first time on this topic, we analyze the historical color palettes of Renaissance oil paintings by using machine-learning methods and digital images. Our work has two main parts: we collect data on their historical color palettes and then use machine learning to predict the original colors of paintings. This model studies color ratios, enhancement levels, symbolic meanings, and historical records. It looks at key colors, measures their relationships, and learns how they have changed. The main contributions of this work are as follows: (i) we develop a model that predicts a painting’s original color palette based on multiple factors, such as the color ratios and symbolic meanings, and (ii) we propose a framework for using cognitive computing tools to recover the original colors of historical artworks. This helps us to rediscover lost emotional and cultural details.