Image augmentation, a foundation in the realm of deep learning, involves artificially expanding a training dataset to enhance model performance and generalization. By creating diverse and realistic variations of existing images, augmentation mitigates the risk of overfitting and enhances the model's ability to handle real-world data complexities (Kumar et al. in Image data augmentation approaches: a comprehensive survey and future directions, 2024). This chapter delves into the applications of deep learning-based image augmentation techniques, exploring their impact on various domains and their potential to revolutionize image-centric tasks. Some of the applications are discussed here.