We shift our focus from structured tabular data to the domain of image data. Synthesizing images addresses specific needs within machine learning, especially for computer vision tasks where acquiring large, varied, and labeled datasets can be challenging.

This chapter introduces the foundational concepts for generating synthetic images. You will examine the reasons synthetic data is valuable in this area, review essential image properties such as pixels and color representations (like $RGB$), and explore methods for creating basic images programmatically, for instance, using simple geometric shapes or patterns. Additionally, we will cover the use of noise and elementary augmentations as synthesis techniques and provide a brief look at rendering simple scenes. We conclude by acknowledging the challenges associated with producing highly realistic synthetic visuals.