Residual Diffusion

Residual diffusion models are a class of generative models leveraging diffusion processes to improve upon existing image and signal processing techniques. Current research focuses on enhancing these models for various applications, including image compression, denoising of medical scans (like PET and MRI), and prediction of disease progression, often employing U-Net or similar architectures with residual connections to improve efficiency and accuracy. This approach offers significant potential for improving the quality and efficiency of data processing across diverse fields, from medical imaging and weather forecasting to document enhancement and video reconstruction.