The field of diffusion models is rapidly advancing, with a focus on developing innovative methods for image and process restoration. Recent research has explored the application of diffusion models to various tasks, including image denoising, deblurring, and stochastic trace recovery. A common thread among these efforts is the use of probabilistic models to learn the underlying dynamics of complex systems, enabling the recovery of high-quality data from noisy or degraded sources. Notably, the development of new diffusion-based frameworks has led to state-of-the-art performance in several applications, including image restoration and motion trajectory estimation. These advances have the potential to impact a wide range of fields, from computer vision to process control. Noteworthy papers include: The Principles of Diffusion Models, which provides a comprehensive foundation for understanding diffusion models. DDTR: Diffusion Denoising Trace Recovery, which demonstrates a novel approach to stochastic trace recovery. Residual Diffusion Bridge Model for Image Restoration, which introduces a unified analytical perspective on diffusion bridge models. MoTDiff: High-resolution Motion Trajectory estimation from a single blurred image using Diffusion models, which achieves high-quality motion trajectory estimation from a single motion-blurred image.