Diffusion Restoration

Diffusion restoration leverages the power of diffusion models to recover high-quality signals from degraded or noisy data, addressing challenges in diverse fields like image and video processing, ultrasound imaging, and speech enhancement. Current research focuses on adapting and improving diffusion model architectures, such as denoising diffusion restoration models (DDRMs), for specific applications, often incorporating techniques like parallel sampling and hybrid approaches combining model-based and learning-based methods to enhance efficiency and generalization. This approach offers significant potential for improving the quality of various signals across numerous applications, leading to advancements in medical imaging, multimedia processing, and other areas requiring high-fidelity signal reconstruction.