This project applies denoising diffusion probabilistic models to solve various image restoration inverse problems. It leverages off-the-shelf diffusion models as implicit priors in plug-and-play frameworks, enabling training-free restoration across tasks like deblurring, inpainting, and super-resolution. The method builds on OpenAI guided diffusion and DPIR, using the diffusion model’s generative capabilities as a flexible denoiser.