Deep learning models in computer vision often rely on large datasets for optimal performance. However, acquiring and annotating extensive image datasets can be time-consuming and expensive. To overcome this limitation, data augmentation techniques have emerged as crucial tools. By artificially expanding the existing dataset through various transformations, image augmentation significantly enhances the training process, improves model robustness, and ultimately leads to better performance. This chapter will delve into the intricacies of deep learning-based image augmentation, exploring advanced techniques that go beyond traditional methods and unlock new possibilities for improving model accuracy and generalization.