Flow matching methods for imaging inverse problems typically incorporate measurements through network conditioning or guidance during sampling. Neither approach explicitly applies the forward operator within the learned conditional velocity field. We develop a principled measurement-conditional velocity parameterizatio...
Shirin Shoushtari, Edward P. Chandler, Xiao Shi et al.· 0 citations
Diffusion and flow-matching models can produce high-quality posterior samples for inverse problems, but typically require tens to thousands of network evaluations per draw. MeanFlow enables one-step generation, yet applying it to inverse problems leaves no intermediate steps at which to enforce measurement consistency....
Shirin Shoushtari, Edward P. Chandler, Xiao Shi et al.· 0 citations
DenseAR is extended to a unified model that handles multiple modalities and imaging tasks within a single backbone that unifies cross-modal translation, modality-conditioned generation, and tumor segmentation, while remaining competitive with task-specific methods.
Chicago Y. Park, Jia-Lin Mao, Xiaojian Xu et al.· arXiv.org· 0 citations
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