The world was captured in black and white until the 1840s. With a Nobel prize in physics in 1908, Gabriel Lippmann started the age of color captures. It was in 1935 when Eastman Kodak came out with an integral tripack color film, called Kodachrome, to capture color photographs.

Color images are not just about aesthetics and beauty, they capture a whole lot more information than black and white images. Color is an important property of real-world objects and it adds another dimension to our perception of the world around us. The importance of colors is such that there have been a number of projects to color historical works of art and photography throughout history. With the advent of tools such as Adobe Photoshop and GIMP, people have been painstakingly turning old photographs into color ones. The reddit r/Colorization subgroup is an online community where people share their experience and work on transforming black and white images into color ones.

So far in this book, we have covered different domains and scenarios to showcase the amazing benefits of transfer learning. In this chapter, we will introduce the concept of image colorization using deep learning and utilize transfer learning to improve upon the results. This chapter will cover the following topics:

• The problem statement
• Understanding image colorization
• Color images
• Building deep neural network-based colorization networks
• Improvements
• Challenges

In the coming sections, we will be using the terms black and white, monochrome, and grayscale to refer to images captured without any color information. We will use these terms interchangeably.