TGD-BO: Task-specific Guidance Design with Bayesian Optimization using unconditional diffusion models for image restoration problems

Motivation: Deep learning models are excellent in image restoration tasks, but often suffer from domain shift and require task-specific learning, which limits their practical use. Goal(s): To develop Task-specific Guidance Design with Bayesian Optimization (TGD-BO) a method that optimizes diffusion guidance parameters for various image restoration tasks without task-specific training. Approach: Conditional generation using data-consistency and gradient guidance, with Bayesian Optimization to efficiently tune guidance parameters through B-spline curve optimization. Results: TGD-BO achieved comparable or superior performance to supervised models across various tasks (compressed sensing, super-resolution, denoising), particularly excelling in joint tasks like simultaneous noise removal and reconstruction. Impact: We proposed a guidance design method adaptable to any image restoration problems and verified its effectiveness. This versatile framework enables diffusion model to handle multiple MRI restoration tasks without task-specific training, potentially serving as a foundation model.