Diffusion Inverse Solver

Diffusion inverse solvers leverage the power of diffusion models to reconstruct data from incomplete or noisy measurements, aiming to improve the accuracy and efficiency of solving inverse problems across various fields. Current research focuses on refining existing diffusion model architectures and developing novel algorithms to reduce the number of computation steps needed for high-quality reconstruction, addressing challenges like handling complex non-linear problems and out-of-distribution data. These advancements have significant implications for diverse applications, including medical imaging, image restoration, and black-box optimization, by enabling more accurate and efficient data reconstruction from limited or imperfect observations.