An extensive survey on Diffusion models

Denoising Diffusion models are gaining growing popularity in the field of generative modeling for several reasons. These reasons include the straightforward and stable training, the outstanding generative quality, and the robust probabilistic foundation, picture synthesis, video production, and molecular design are all examples of what this tool can do. This thesis explores denoising diffusion models, which are statistical models that aim to remove noise from an image while preserving its important features. The study focuses on developing new techniques for improving the performance of denoising diffusion models, such as incorporating prior information about the image structure, designing more efficient numerical algorithms for solving the models, and evaluating the effectiveness of the denoising algorithms using various quality metrics. The research also investigates the application of denoising diffusion models in various image processing tasks, such as image restoration, feature extraction, and segmentation. The performance of the proposed methods is evaluated on a variety of benchmark datasets, and the results demonstrate significant improvements in denoising accuracy compared to existing state-of-the-art techniques. Overall, this thesis provides valuable insights into the development and application of denoising diffusion models, which have important applications in many fields, including medical imaging, computer vision, and remote sensing. The proposed techniques and algorithms can potentially lead to significant advances in image processing and analysis, with practical implications for improving the quality and reliability of image-based applications.