Image colorization

Imagine a scenario where you are given a bunch of black-and-white images and are asked to turn them into color images. How would you solve this problem? One way to solve this is by using a pseudo-supervised pipeline where we take a raw image, convert it into black and white, and treat them as input-output pairs. We will demonstrate this by leveraging the CIFAR-10 dataset to perform colorization on images.

The strategy that we will adopt as we code up the image colorization network is as follows:

1. Take the original color image in the training dataset and convert it into grayscale to fetch the input (grayscale) and output (original colored image) combination.
2. Normalize the input and output.
3. Build a U-Net architecture.
4. Train the model over increasing epochs.

With the preceding strategy in place, let's go ahead and code up the model as follows:

The following code is available as Image colorization.ipynb in the Chapter 10 folder of this book's GitHub repository...