Denoising Diffusion Restoration

Denoising Diffusion Restoration Models (DDRMs) leverage pre-trained diffusion models to solve inverse problems in image processing and other fields, aiming to reconstruct high-quality signals from noisy or incomplete data. Current research focuses on adapting DDRMs to various modalities, including medical imaging (e.g., diffusion MRI, ultrasound) and tackling both linear and non-linear inverse problems, often employing techniques like singular value decomposition or pseudo-inverse operators. This approach offers a powerful, unsupervised framework for image restoration and signal reconstruction, improving accuracy and efficiency compared to traditional methods and showing promise for diverse applications across scientific disciplines and practical technologies.