FIRM: Flow-based Imaging via Regularized Minimization
This work proposes a new flow-matching method for inverse problems by embedding the forward operator directly into learned dynamics. It learns an optimal measurement-conditional velocity field that guides a probability flow from an initial source distribution to the data-consistent posterior distribution, achieving state-of-the-art reconstruction with fewer function evaluations.