We're now going to work on another practical application of autoencoders. In this case, we're going to imagine that we have a grayscale photo and that we want to build a tool that will automatically add color to them. We would like to replicate the human abilities in identifying that the sea and sky are blue, the grass field and trees are green, while clouds are white, and so on.
As shown in Figure 3.4.1, if we are given a grayscale photo of a rice field on the foreground, a volcano in the background and sky on top, we're able to add the appropriate colors.
Figure 3.4.1: Adding color to a grayscale photo of the Mayon Volcano. A colorization network should replicate human abilities by adding color to a grayscale photo. Left photo is grayscale. The right photo is color. Original color photo can be found on the book GitHub repository, https://github.com/PacktPublishing/Advanced-Deep-Learning-with-Keras/blob/master/chapter3-autoencoders/README.md.
A simple automatic...
