A Comprehensive Survey on Diffusion Models and Their Applications
It addresses the need for a broad overview of diffusion models across various fields, which is incremental as it synthesizes existing literature without introducing new methods.
This paper provides a comprehensive survey of diffusion models, covering their theoretical foundations and applications across multiple domains like image processing and healthcare, aiming to consolidate knowledge and guide future research.
Diffusion Models are probabilistic models that create realistic samples by simulating the diffusion process, gradually adding and removing noise from data. These models have gained popularity in domains such as image processing, speech synthesis, and natural language processing due to their ability to produce high-quality samples. As Diffusion Models are being adopted in various domains, existing literature reviews that often focus on specific areas like computer vision or medical imaging may not serve a broader audience across multiple fields. Therefore, this review presents a comprehensive overview of Diffusion Models, covering their theoretical foundations and algorithmic innovations. We highlight their applications in diverse areas such as media quality, authenticity, synthesis, image transformation, healthcare, and more. By consolidating current knowledge and identifying emerging trends, this review aims to facilitate a deeper understanding and broader adoption of Diffusion Models and provide guidelines for future researchers and practitioners across diverse disciplines.