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In parallel, augmentation approaches can also be used for editing\/modifying a given image in a context- and semantics-aware way. Diffusion Models (DMs), which comprise one of the most recent and highly promising classes of methods in the field of generative Artificial Intelligence (AI), have emerged as a powerful tool for image data augmentation, capable of generating realistic and diverse images by learning the underlying data distribution. The current study realizes a systematic, comprehensive and in-depth review of DM-based approaches for image augmentation, covering a wide range of strategies, tasks and applications. In particular, a comprehensive analysis of the fundamental principles, model architectures and training strategies of DMs is initially performed. Subsequently, a taxonomy of the relevant image augmentation methods is introduced, focusing on techniques regarding semantic manipulation, personalization and adaptation, and application-specific augmentation tasks. Then, performance assessment methodologies and respective evaluation metrics are analyzed. Finally, current challenges and future research directions in the field are discussed.<\/jats:p>","DOI":"10.1007\/s10462-025-11116-x","type":"journal-article","created":{"date-parts":[[2025,1,30]],"date-time":"2025-01-30T03:17:29Z","timestamp":1738207049000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":49,"title":["Advances in diffusion models for image data augmentation: a review of methods, models, evaluation metrics and future research directions"],"prefix":"10.1007","volume":"58","author":[{"given":"Panagiotis","family":"Alimisis","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ioannis","family":"Mademlis","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Panagiotis","family":"Radoglou-Grammatikis","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Panagiotis","family":"Sarigiannidis","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Georgios Th.","family":"Papadopoulos","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2025,1,30]]},"reference":[{"key":"11116_CR1","unstructured":"Ackermann J, Li M (2022) High-resolution image editing via multi-stage blended diffusion. arXiv preprint arXiv:2210.12965"},{"key":"11116_CR2","unstructured":"Agustsson E, Mentzer F, Tschannen M, et al. 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